--------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\kab235\Dropbox\CovidPopulism\Submi
> ssion PSRM\BaldwinMares_DataReplication\Covidrisk_appe
> ndix_replication.log
  log type:  text
 opened on:  20 Jul 2022, 12:54:11

. 
. 
. global esttabformat b(%8.2f) se(%8.2f) obs r2(%8.2f) s
> tar(+ 0.10 * 0.05 ** 0.01)

. 
. 
. ************
. *APPENDIX D*
. ************
. 
. 
. use "data/Covidrisk_wave1.dta", replace

. 
. *statistics on overall attention rates referenced in t
> ext*
. sum passedAttentionCheck1 passedAttentionCheck2 passed
> one passedboth

    Variable |        Obs        Mean    Std. Dev.      
>  Min        Max
-------------+------------------------------------------
> ---------------
passedAtte~1 |      1,996    .7114228    .4532144       
>    0          1
passedAtte~2 |      1,996    .7194389     .449386       
>    0          1
   passedone |      1,996    .8562124    .3509621       
>    0          1
  passedboth |      1,996    .5746493      .49452       
>    0          1

. 
. **********
. *TABLE D1*
. **********
. *statistics on attention rates by subgroup included in
>  Table D1*
. sum passedAttentionCheck1 passedAttentionCheck2 passed
> one passedboth if Democrat==0 & Republican==0

    Variable |        Obs        Mean    Std. Dev.      
>  Min        Max
-------------+------------------------------------------
> ---------------
passedAtte~1 |        267    .8202247    .3847213       
>    0          1
passedAtte~2 |        267    .7790262     .415682       
>    0          1
   passedone |        267     .928839    .2575765       
>    0          1
  passedboth |        267     .670412    .4709463       
>    0          1

. sum passedAttentionCheck1 passedAttentionCheck2 passed
> one passedboth if Democrat==1

    Variable |        Obs        Mean    Std. Dev.      
>  Min        Max
-------------+------------------------------------------
> ---------------
passedAtte~1 |        449    .7416481    .4382167       
>    0          1
passedAtte~2 |        449    .7817372    .4135275       
>    0          1
   passedone |        449    .8908686    .3121518       
>    0          1
  passedboth |        449    .6325167    .4826574       
>    0          1

. sum passedAttentionCheck1 passedAttentionCheck2 passed
> one passedboth if Republican==1

    Variable |        Obs        Mean    Std. Dev.      
>  Min        Max
-------------+------------------------------------------
> ---------------
passedAtte~1 |        467    .6959315    .4605051       
>    0          1
passedAtte~2 |        467    .7087794    .4548121       
>    0          1
   passedone |        467    .8458244    .3615042       
>    0          1
  passedboth |        467    .5588865    .4970527       
>    0          1

. 
. 
. ************
. *APPENDIX E*
. ************
. 
. use "data/Covidrisk_wave2.dta", replace

. 
. *statistics on overall attention rates referenced in t
> ext*
. sum passedattention1 passedattention2 passedone passed
> both

    Variable |        Obs        Mean    Std. Dev.      
>  Min        Max
-------------+------------------------------------------
> ---------------
passedatte~1 |      2,507    .5548464    .4970819       
>    0          1
passedatte~2 |      2,507    .6234543     .484616       
>    0          1
   passedone |      2,507    .7319505    .4430319       
>    0          1
  passedboth |      2,507    .4463502    .4972125       
>    0          1

. 
. **********
. *TABLE E1*
. **********
. *statistics on attention rates by subgroup included in
>  Table E1*
. sum passedattention1 passedattention2 passedone passed
> both if (independent=="1" | independent=="2")

    Variable |        Obs        Mean    Std. Dev.      
>  Min        Max
-------------+------------------------------------------
> ---------------
passedatte~1 |        501    .6487026    .4778528       
>    0          1
passedatte~2 |        501    .6566866    .4752896       
>    0          1
   passedone |        501    .7964072     .403072       
>    0          1
  passedboth |        501     .508982     .500419       
>    0          1

. sum passedattention1 passedattention2 passedone passed
> both if (democrat=="1" | democrat=="2")

    Variable |        Obs        Mean    Std. Dev.      
>  Min        Max
-------------+------------------------------------------
> ---------------
passedatte~1 |        819    .6227106    .4850045       
>    0          1
passedatte~2 |        819    .6800977    .4667235       
>    0          1
   passedone |        819    .7838828    .4118467       
>    0          1
  passedboth |        819    .5189255     .499947       
>    0          1

. sum passedattention1 passedattention2 passedone passed
> both if (republican=="1" |republican=="2")

    Variable |        Obs        Mean    Std. Dev.      
>  Min        Max
-------------+------------------------------------------
> ---------------
passedatte~1 |      1,166    .4656947    .4990358       
>    0          1
passedatte~2 |      1,166    .5686106    .4954827       
>    0          1
   passedone |      1,166    .6680961    .4710988       
>    0          1
  passedboth |      1,166    .3662093    .4819743       
>    0          1

. 
. ***********
. *FIGURE E1*
. *SUBJECTIVE RISK BY TREATMENT*
. ******************************
. 
. 
. set scheme plotplain

. 
. 
. preserve

. 
. collapse (mean) meanrisk= risk (sd) sdrisk=risk (count
> ) n=risk, by(treatment)

. 
. generate hirisk = meanrisk + invttail(n-1,0.025)*(sdri
> sk / sqrt(n))

. generate lowrisk = meanrisk - invttail(n-1,0.025)*(sdr
> isk / sqrt(n))

. 
. 
. graph twoway (bar meanrisk treatment if treatment==0) 
> (bar meanrisk treatment if treatment==1) (bar meanrisk
>  treatment if treatment==2) (bar meanrisk treatment if
>  treatment==3) (rcap hirisk lowrisk treatment),  ytitl
> e("Average Subjective Insecurity") xlabel(0 "Control" 
> 1 "Tech" 2 "Anti-Foreign" 3 "Anti-Elite", noticks) xti
> tle("") title("Average Subjective Insecurity by Treatm
> ent") legend( order(1 "Control" 2 "Technocratic" 3 "An
> ti-Foreign" 4 "Anti-Elite") )

. graph export "figures_tables/FigureE1.tif", replace
(file figures_tables/FigureE1.tif written in TIFF format
> )

.            
. restore

. 
. ***********
. *FIGURE E2*
. *RESPONDENT ANTI-TRADE AND PRO-IMMIGRATION ATTITUDES B
> Y TREATMENT*
. ******************************************************
> *************
. 
. preserve

. 
. collapse (mean) meanantifor= trade (sd) sdantifor=trad
> e (count) n=trade, by(treatment)

. 
. generate hiantifor = meanantifor + invttail(n-1,0.025)
> *(sdantifor / sqrt(n))

. generate lowantifor = meanantifor - invttail(n-1,0.025
> )*(sdantifor / sqrt(n))

. 
. 
. graph twoway (bar meanantifor treatment if treatment==
> 0) (bar meanantifor treatment if treatment==1) (bar me
> anantifor treatment if treatment==2) (bar meanantifor 
> treatment if treatment==3) (rcap hiantifor lowantifor 
> treatment),  ytitle("Average Anti-Trade Attitudes") xl
> abel(0 "Control" 1 "Tech" 2 "Anti-Foreign" 3 "Anti-Eli
> te", noticks) xtitle("") title("Average Anti-Trade Att
> itudes by Treatment") legend( order(1 "Control" 2 "Tec
> hnocratic" 3 "Anti-Foreign" 4 "Anti-Elite") )

. graph export "figures_tables/FigureE2_left.tif", repla
> ce
(file figures_tables/FigureE2_left.tif written in TIFF f
> ormat)

.            
. restore

. 
. 
. preserve

. 
. collapse (mean) meanantifor= immigration (sd) sdantifo
> r=immigration (count) n=immigration, by(treatment)

. 
. generate hiantifor = meanantifor + invttail(n-1,0.025)
> *(sdantifor / sqrt(n))

. generate lowantifor = meanantifor - invttail(n-1,0.025
> )*(sdantifor / sqrt(n))

. 
. 
. graph twoway (bar meanantifor treatment if treatment==
> 0) (bar meanantifor treatment if treatment==1) (bar me
> anantifor treatment if treatment==2) (bar meanantifor 
> treatment if treatment==3) (rcap hiantifor lowantifor 
> treatment),  ytitle("Average Pro-Immigrant Attitudes")
>  xlabel(0 "Control" 1 "Tech" 2 "Anti-Foreign" 3 "Anti-
> Elite", noticks) xtitle("") title("Average Pro-Immigra
> nt Attitudes by Treatment") legend( order(1 "Control" 
> 2 "Technocratic" 3 "Anti-Foreign" 4 "Anti-Elite") )

. graph export "figures_tables/FigureE2_right.tif", repl
> ace
(file figures_tables/FigureE2_right.tif written in TIFF 
> format)

. 
. restore

. 
. **********
. *TABLE E2*
. *REPLICATION OF MAIN RESULTS (DEMOCRAT AND INDEPENDENT
>  SAMPLE ONLY)*
. ******************************************************
> **************
. 
. preserve

. 
. keep if (democrat=="1" | democrat=="2" | independent==
> "1" | independent=="2")
(1,187 observations deleted)

. 
. eststo m1a: reg pandemicsupport tech for elite knowCov
> id, robust

Linear regression                               Number o
> f obs     =      1,320
                                                F(4, 131
> 5)        =       3.02
                                                Prob > F
>           =     0.0171
                                                R-square
> d         =     0.0106
                                                Root MSE
>           =     .96144

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |  -.1078022   .0819626    -1.32   0.189   
>   -.268594                                            
>               .0529896
         for |  -.1024731   .0820067    -1.25   0.212   
>  -.2633515                                            
>               .0584052
       elite |  -.1877847   .0828062    -2.27   0.024   
>  -.3502314                                            
>               -.025338
   knowCovid |  -.2209781   .0844911    -2.62   0.009   
>  -.3867301                                            
>               -.055226
       _cons |   3.769127   .0695151    54.22   0.000   
>   3.632754                                            
>               3.905499
--------------------------------------------------------
> ----------------------

. eststo m1b: reg pandemicsupport tech for elite techXkn
> ow forXknow eliteXknow knowCovid, robust

Linear regression                               Number o
> f obs     =      1,320
                                                F(7, 131
> 2)        =       2.31
                                                Prob > F
>           =     0.0241
                                                R-square
> d         =     0.0152
                                                Root MSE
>           =     .96032

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |  -.0033701     .10789    -0.03   0.975   
>  -.2150259                                            
>               .2082857
         for |  -.0392637    .113554    -0.35   0.730   
>  -.2620309                                            
>               .1835035
       elite |   .0004247   .1107368     0.00   0.997   
>  -.2168158                                            
>               .2176652
   techXknow |  -.3230644   .2690895    -1.20   0.230   
>  -.8509572                                            
>               .2048283
    forXknow |  -.2084593    .261452    -0.80   0.425   
>  -.7213691                                            
>               .3044504
  eliteXknow |  -.5598163   .2632954    -2.13   0.034   
>  -1.076342                                            
>              -.0432902
   knowCovid |   .0831963   .2119139     0.39   0.695   
>  -.3325307                                            
>               .4989233
       _cons |   3.671249   .0880863    41.68   0.000   
>   3.498443                                            
>               3.844054
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (elite + eliteXknow)

 ( 1)  for - elite + forXknow - eliteXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .3116686   .1660151     1.88   0.061   
>  -.0140154                                            
>               .6373526
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (tech + techXknow)

 ( 1)  - tech + for - techXknow + forXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .0787115   .1746726     0.45   0.652   
>  -.2639565                                            
>               .4213796
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXknow) - (tech + techXknow)

 ( 1)  - tech + elite - techXknow + eliteXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   -.232957   .1808647    -1.29   0.198   
>  -.5877727                                            
>               .1218586
--------------------------------------------------------
> ----------------------

. eststo m1c: reg pandemicsupport tech for elite jobeffe
> ctCovid3, robust

Linear regression                               Number o
> f obs     =      1,320
                                                F(4, 131
> 5)        =       1.48
                                                Prob > F
>           =     0.2055
                                                R-square
> d         =     0.0044
                                                Root MSE
>           =     .96446

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |  -.1133619   .0818996    -1.38   0.167   
>  -.2740301                                            
>               .0473062
         for |  -.1107907   .0815848    -1.36   0.175   
>  -.2708413                                            
>               .0492599
       elite |  -.1953365    .082856    -2.36   0.019   
>  -.3578809                                            
>              -.0327921
jobeffectC~3 |  -.0569173   .0908883    -0.63   0.531   
>  -.2352193                                            
>               .1213846
       _cons |   3.714799   .0697703    53.24   0.000   
>   3.577926                                            
>               3.851672
--------------------------------------------------------
> ----------------------

. eststo m1d: reg pandemicsupport tech for elite techXjo
> beffectCovid3 forXjobeffectCovid3 eliteXjobeffectCovid
> 3 jobeffectCovid3, robust

Linear regression                               Number o
> f obs     =      1,320
                                                F(7, 131
> 2)        =       1.22
                                                Prob > F
>           =     0.2858
                                                R-square
> d         =     0.0068
                                                Root MSE
>           =     .96439

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |  -.0943149   .1018417    -0.93   0.355   
>  -.2941054                                            
>               .1054755
         for |  -.1509393    .102836    -1.47   0.142   
>  -.3526803                                            
>               .0508016
       elite |  -.1166065   .1049688    -1.11   0.267   
>  -.3225316                                            
>               .0893186
techXjobef~3 |  -.0646012   .2502559    -0.26   0.796   
>  -.5555466                                            
>               .4263442
forXjobeff~3 |    .130098   .2319862     0.56   0.575   
>  -.3250065                                            
>               .5852024
eliteXjobe~3 |  -.2752656   .2423273    -1.14   0.256   
>  -.7506569                                            
>               .2001256
jobeffectC~3 |  -.0207929   .1632226    -0.13   0.899   
>  -.3409987                                            
>               .2994128
       _cons |    3.70415   .0788814    46.96   0.000   
>   3.549402                                            
>               3.858897
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (elite + eliteXjo
> beffectCovid3)

 ( 1)  for - elite + forXjobeffectCovid3 -
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .3710308   .1928952     1.92   0.055   
>   -.007386                                            
>               .7494475
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (tech + techXjobe
> ffectCovid3)

 ( 1)  - tech + for - techXjobeffectCovid3 +
       forXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .1380748   .2023848     0.68   0.495   
>  -.2589584                                            
>               .5351079
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXjobeffectCovid3) - (tech + techX
> jobeffectCovid3)

 ( 1)  - tech + elite - techXjobeffectCovid3 +
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   -.232956   .2101353    -1.11   0.268   
>  -.6451939                                            
>               .1792819
--------------------------------------------------------
> ----------------------

. 
. esttab m1a m1b m1c m1d using "figures_tables/Appendixt
> ableE2.rtf", order(for elite tech knowCovid forXknow e
> liteXknow techXknow jobeffectCovid3 forXjobeffectCovid
> 3 eliteXjobeffectCovid3 techXjobeffectCovid3) $esttabf
> ormat replace label onecell
(output written to figures_tables/AppendixtableE2.rtf)

. *Note: lincom estimates added manually to table*
. 
. set scheme tufte

. 
. reg pandemicsupport knowCovid for elite tech forXknow 
> eliteXknow techXknow, robust

Linear regression                               Number o
> f obs     =      1,320
                                                F(7, 131
> 2)        =       2.31
                                                Prob > F
>           =     0.0241
                                                R-square
> d         =     0.0152
                                                Root MSE
>           =     .96032

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
   knowCovid |   .0831963   .2119139     0.39   0.695   
>  -.3325307                                            
>               .4989233
         for |  -.0392637    .113554    -0.35   0.730   
>  -.2620309                                            
>               .1835035
       elite |   .0004247   .1107368     0.00   0.997   
>  -.2168158                                            
>               .2176652
        tech |  -.0033701     .10789    -0.03   0.975   
>  -.2150259                                            
>               .2082857
    forXknow |  -.2084593    .261452    -0.80   0.425   
>  -.7213691                                            
>               .3044504
  eliteXknow |  -.5598163   .2632954    -2.13   0.034   
>  -1.076342                                            
>              -.0432902
   techXknow |  -.3230644   .2690895    -1.20   0.230   
>  -.8509572                                            
>               .2048283
       _cons |   3.671249   .0880863    41.68   0.000   
>   3.498443                                            
>               3.844054
--------------------------------------------------------
> ----------------------

. 
. predictnl diff_1 = (_b[for] + _b[forXknow]*knowCovid) 
> - (_b[elite] + _b[eliteXknow]*knowCovid), se(diff1_se)

. 
. gen upper1 = diff_1 + diff1_se*1.96

. gen lower1 = diff_1 - diff1_se*1.96

. 
. twoway (line diff_1 upper1 lower1 knowCovid, sort lcol
> or(gs2) lpattern(solid dash dash)), yline(0, lcolor(gs
> 2) lpattern(dot)) legend(position(5) ring(0)) xtitle("
> Exposure to Health Shock", color(gs2)) ///
>        ytitle("Differential Effect of Anti-Foreign vs.
>  Anti-Elite", color(gs2)) ylabel(-0.5 (0.5) 2,nogrid) 
> yscale(range(-0.5 (0.5) 2)) ///
>            xlabel (0 (0.5) 1) graphregion(fcolor(white
> ) ifcolor(white) color(white) lcolor(white) ilcolor(wh
> ite)) ///
>            legend(order(1 "Effect" 2 "95 % CIs"))

. graph export "figures_tables/FigureE3_left.tif", repla
> ce
(file figures_tables/FigureE3_left.tif written in TIFF f
> ormat)

. 
. reg pandemicsupport jobeffectCovid3 for elite tech for
> XjobeffectCovid3 eliteXjobeffectCovid3 techXjobeffectC
> ovid3, robust

Linear regression                               Number o
> f obs     =      1,320
                                                F(7, 131
> 2)        =       1.22
                                                Prob > F
>           =     0.2858
                                                R-square
> d         =     0.0068
                                                Root MSE
>           =     .96439

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
jobeffectC~3 |  -.0207929   .1632226    -0.13   0.899   
>  -.3409987                                            
>               .2994128
         for |  -.1509393    .102836    -1.47   0.142   
>  -.3526803                                            
>               .0508016
       elite |  -.1166065   .1049688    -1.11   0.267   
>  -.3225316                                            
>               .0893186
        tech |  -.0943149   .1018417    -0.93   0.355   
>  -.2941054                                            
>               .1054755
forXjobeff~3 |    .130098   .2319862     0.56   0.575   
>  -.3250065                                            
>               .5852024
eliteXjobe~3 |  -.2752656   .2423273    -1.14   0.256   
>  -.7506569                                            
>               .2001256
techXjobef~3 |  -.0646012   .2502559    -0.26   0.796   
>  -.5555466                                            
>               .4263442
       _cons |    3.70415   .0788814    46.96   0.000   
>   3.549402                                            
>               3.858897
--------------------------------------------------------
> ----------------------

. 
. predictnl diff_2 =  (_b[for] +_b[forXjobeffectCovid3]*
> jobeffectCovid3) - (_b[elite]+ _b[eliteXjobeffectCovid
> 3]*jobeffectCovid3), se(diff2_se)

. 
. gen upper2 = diff_2 + diff2_se*1.96

. gen lower2 = diff_2 - diff2_se*1.96

. 
. twoway (line diff_2 upper2 lower2 jobeffectCovid3, sor
> t lcolor(gs2) lpattern(solid dash dash)), yline(0, lco
> lor(gs2) lpattern(dot)) legend(position(5) ring(0)) xt
> itle("Exposure to Economic Shock", color(gs2)) ///
>        ytitle("Differential Effect of Anti-Foreign vs.
>  Anti-Elite", color(gs2)) ylabel(-0.5 (0.5) 2,nogrid) 
> yscale(range(-0.5 (0.5) 2)) ///
>            graphregion(fcolor(white) ifcolor(white) co
> lor(white) lcolor(white) ilcolor(white)) ///
>            legend(order(1 "Effect" 2 "95 % CIs"))

. graph export "figures_tables/FigureE3_right.tif", repl
> ace
(file figures_tables/FigureE3_right.tif written in TIFF 
> format)

. 
. restore

. 
. 
. ************
. *APPENDIX F*
. ************
. 
. use "data/Covidrisk_wave1.dta", replace

. 
. *****************************************************
. *TABLE F1: CORRELATES OF HEALTH AND EMPLOYMENT SHOCK*
. *****************************************************
. 
. eststo m1a: reg knowCovid female age hispanic_di black
>  hhi trumpsupporter HighSchoolorLess CollegeDegree Gra
> duateDegree, robust

Linear regression                               Number o
> f obs     =      1,686
                                                F(9, 167
> 6)        =      41.43
                                                Prob > F
>           =     0.0000
                                                R-square
> d         =     0.2014
                                                Root MSE
>           =     .32815

--------------------------------------------------------
> ----------------------
             |               Robust
   knowCovid |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
      female |  -.0281126   .0164457    -1.71   0.088   
>  -.0603689                                            
>               .0041437
         age |  -.0056241   .0005217   -10.78   0.000   
>  -.0066472                                            
>              -.0046009
 hispanic_di |   .1633557    .035917     4.55   0.000   
>   .0929087                                            
>               .2338027
       black |   .0880882   .0359281     2.45   0.014   
>   .0176195                                            
>               .1585568
         hhi |   .0037233   .0012962     2.87   0.004   
>    .001181                                            
>               .0062655
trumpsuppo~r |   .0691463   .0161751     4.27   0.000   
>   .0374207                                            
>               .1008718
HighSchool~s |  -.0414937   .0221902    -1.87   0.062   
>  -.0850171                                            
>               .0020296
CollegeDeg~e |   .0155284   .0204628     0.76   0.448   
>   -.024607                                            
>               .0556638
GraduateDe~e |    .119683   .0245334     4.88   0.000   
>   .0715637                                            
>               .1678023
       _cons |   .4819049   .0394596    12.21   0.000   
>   .4045096                                            
>               .5593003
--------------------------------------------------------
> ----------------------

. eststo m1b: reg jobeffectCovid3 female age hispanic_di
>  black hhi trumpsupporter HighSchoolorLess CollegeDegr
> ee GraduateDegree, robust

Linear regression                               Number o
> f obs     =      1,686
                                                F(9, 167
> 6)        =      43.51
                                                Prob > F
>           =     0.0000
                                                R-square
> d         =     0.2079
                                                Root MSE
>           =     .33075

--------------------------------------------------------
> ----------------------
             |               Robust
jobeffectC~3 |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
      female |  -.0583613   .0180033    -3.24   0.001   
>  -.0936726                                            
>              -.0230501
         age |  -.0064375   .0005145   -12.51   0.000   
>  -.0074466                                            
>              -.0054284
 hispanic_di |   .2122699   .0540788     3.93   0.000   
>   .1062007                                            
>               .3183391
       black |   .1193499   .0427701     2.79   0.005   
>   .0354614                                            
>               .2032384
         hhi |   .0012458   .0014877     0.84   0.402   
>  -.0016721                                            
>               .0041637
trumpsuppo~r |   .0600011   .0163311     3.67   0.000   
>   .0279696                                            
>               .0920326
HighSchool~s |  -.0281311   .0221154    -1.27   0.204   
>  -.0715078                                            
>               .0152455
CollegeDeg~e |  -.0196477   .0206564    -0.95   0.342   
>  -.0601628                                            
>               .0208675
GraduateDe~e |   .0975038   .0254397     3.83   0.000   
>   .0476068                                            
>               .1474008
       _cons |   .5500083   .0425211    12.93   0.000   
>   .4666081                                            
>               .6334084
--------------------------------------------------------
> ----------------------

. 
. esttab m1a m1b using "figures_tables/AppendixtableF1.r
> tf", $esttabformat replace label onecell
(output written to figures_tables/AppendixtableF1.rtf)

. 
. 
. *******************
. *TABLE F3: BALANCE*
. *******************
. 
. global DESCVARS female age hispanic_di black hhi trump
> supporter HighSchoolorLess CollegeDegree GraduateDegre
> e knowCovid jobeffectCovid3

. mata: mata clear

. 
. * Differences: (2)-(1)/Technocratic-Control
. 
. local i = 1

. 
. foreach var in $DESCVARS {
  2.     reg `var' tech if (tech==1 | control==1), robus
> t    
  3.     outreg, keep(tech)  rtitle("`: var label `var''
> ") stats(b) ///
>         noautosumm store(row`i')  starlevels(10 5 1) s
> tarloc(1)
  4.     outreg, replay(diff) append(row`i') ctitles("",
> Difference ) ///
>         store(diff) note("")
  5.     local ++i
  6. }

Linear regression                               Number o
> f obs     =        856
                                                F(1, 854
> )         =       1.15
                                                Prob > F
>           =     0.2828
                                                R-square
> d         =     0.0014
                                                Root MSE
>           =     .50011

--------------------------------------------------------
> ----------------------
             |               Robust
      female |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |  -.0385132   .0358363    -1.07   0.283   
>  -.1088508                                            
>               .0318244
       _cons |   .5133333    .028891    17.77   0.000   
>   .4566276                                            
>               .5700391
--------------------------------------------------------
> ----------------------

                   ------------------
                             female 
                   ------------------
                    Female   -0.039 
                   ------------------
             * p<0.1; ** p<0.05; *** p<0.01

warning: no table named row1 found with which to merge o
> r append

                -----------------------
                          Difference  
                -----------------------
                 Female     -0.039    
                -----------------------
                            


Linear regression                               Number o
> f obs     =        856
                                                F(1, 854
> )         =       0.02
                                                Prob > F
>           =     0.8876
                                                R-square
> d         =     0.0000
                                                Root MSE
>           =     16.793

--------------------------------------------------------
> ----------------------
             |               Robust
         age |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |    .173693    1.22843     0.14   0.888   
>  -2.237402                                            
>               2.584788
       _cons |   52.37667   1.013522    51.68   0.000   
>   50.38738                                            
>               54.36595
--------------------------------------------------------
> ----------------------

                     --------------
                             age  
                     --------------
                      Age   0.174 
                     --------------
             * p<0.1; ** p<0.05; *** p<0.01


                -----------------------
                          Difference  
                -----------------------
                 Female     -0.039    
                 Age         0.174    
                -----------------------
                            


Linear regression                               Number o
> f obs     =        856
                                                F(1, 854
> )         =       0.43
                                                Prob > F
>           =     0.5117
                                                R-square
> d         =     0.0005
                                                Root MSE
>           =     .26884

--------------------------------------------------------
> ----------------------
             |               Robust
 hispanic_di |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |  -.0129257   .0196877    -0.66   0.512   
>  -.0515676                                            
>               .0257163
       _cons |   .0866667   .0162625     5.33   0.000   
>   .0547475                                            
>               .1185859
--------------------------------------------------------
> ----------------------

               -------------------------
                           hispanic_di 
               -------------------------
                Hispanic     -0.013    
               -------------------------
             * p<0.1; ** p<0.05; *** p<0.01


               -------------------------
                           Difference  
               -------------------------
                Female       -0.039    
                Age           0.174    
                Hispanic     -0.013    
               -------------------------
                            


Linear regression                               Number o
> f obs     =        856
                                                F(1, 854
> )         =       0.09
                                                Prob > F
>           =     0.7651
                                                R-square
> d         =     0.0001
                                                Root MSE
>           =     .26141

--------------------------------------------------------
> ----------------------
             |               Robust
       black |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |   .0055396   .0185311     0.30   0.765   
>  -.0308323                                            
>               .0419114
       _cons |        .07   .0147482     4.75   0.000   
>   .0410531                                            
>               .0989469
--------------------------------------------------------
> ----------------------

                    ----------------
                             black 
                    ----------------
                     Black   0.006 
                    ----------------
             * p<0.1; ** p<0.05; *** p<0.01


               -------------------------
                           Difference  
               -------------------------
                Female       -0.039    
                Age           0.174    
                Hispanic     -0.013    
                Black         0.006    
               -------------------------
                            


Linear regression                               Number o
> f obs     =        856
                                                F(1, 854
> )         =       2.17
                                                Prob > F
>           =     0.1412
                                                R-square
> d         =     0.0025
                                                Root MSE
>           =     7.0027

--------------------------------------------------------
> ----------------------
             |               Robust
         hhi |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |     .73753   .5007879     1.47   0.141   
>  -.2453893                                            
>               1.720449
       _cons |   12.68333   .4027824    31.49   0.000   
>   11.89277                                            
>               13.47389
--------------------------------------------------------
> ----------------------

             -----------------------------
                                    hhi  
             -----------------------------
              HH Income Category   0.738 
             -----------------------------
             * p<0.1; ** p<0.05; *** p<0.01


          -----------------------------------
                                Difference  
          -----------------------------------
           Female                 -0.039    
           Age                     0.174    
           Hispanic               -0.013    
           Black                   0.006    
           HH Income Category      0.738    
          -----------------------------------
                            


Linear regression                               Number o
> f obs     =        856
                                                F(1, 854
> )         =       0.74
                                                Prob > F
>           =     0.3891
                                                R-square
> d         =     0.0009
                                                Root MSE
>           =     .49818

--------------------------------------------------------
> ----------------------
             |               Robust
trumpsuppo~r |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |   .0306954   .0356202     0.86   0.389   
>  -.0392179                                            
>               .1006088
       _cons |   .4333333   .0286432    15.13   0.000   
>   .3771139                                            
>               .4895527
--------------------------------------------------------
> ----------------------

  ---------------------------------------------------
                                     trumpsupporter 
  ---------------------------------------------------
   Voted for Trump (2016 Election)       0.031      
  ---------------------------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                              -0.039    
     Age                                  0.174    
     Hispanic                            -0.013    
     Black                                0.006    
     HH Income Category                   0.738    
     Voted for Trump (2016 Election)      0.031    
    ------------------------------------------------
                            


Linear regression                               Number o
> f obs     =        729
                                                F(1, 727
> )         =       0.07
                                                Prob > F
>           =     0.7843
                                                R-square
> d         =     0.0001
                                                Root MSE
>           =     .37378

--------------------------------------------------------
> ----------------------
             |               Robust
HighSchool~s |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |  -.0079921   .0291939    -0.27   0.784   
>  -.0653064                                            
>               .0493223
       _cons |    .172549   .0236948     7.28   0.000   
>   .1260305                                            
>               .2190675
--------------------------------------------------------
> ----------------------

       -----------------------------------------
                              HighSchoolorLess 
       -----------------------------------------
        High School or Less        -0.008      
       -----------------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                              -0.039    
     Age                                  0.174    
     Hispanic                            -0.013    
     Black                                0.006    
     HH Income Category                   0.738    
     Voted for Trump (2016 Election)      0.031    
     High School or Less                 -0.008    
    ------------------------------------------------
                            


Linear regression                               Number o
> f obs     =        729
                                                F(1, 727
> )         =       0.49
                                                Prob > F
>           =     0.4857
                                                R-square
> d         =     0.0007
                                                Root MSE
>           =     .49607

--------------------------------------------------------
> ----------------------
             |               Robust
CollegeDeg~e |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |   .0269298   .0386046     0.70   0.486   
>    -.04886                                            
>               .1027195
       _cons |   .5490196   .0312032    17.59   0.000   
>   .4877605                                            
>               .6102788
--------------------------------------------------------
> ----------------------

           ---------------------------------
                             CollegeDegree 
           ---------------------------------
            College Degree       0.027     
           ---------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                              -0.039    
     Age                                  0.174    
     Hispanic                            -0.013    
     Black                                0.006    
     HH Income Category                   0.738    
     Voted for Trump (2016 Election)      0.031    
     High School or Less                 -0.008    
     College Degree                       0.027    
    ------------------------------------------------
                            


Linear regression                               Number o
> f obs     =        729
                                                F(1, 727
> )         =       0.16
                                                Prob > F
>           =     0.6930
                                                R-square
> d         =     0.0002
                                                Root MSE
>           =     .43803

--------------------------------------------------------
> ----------------------
             |               Robust
GraduateDe~e |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |  -.0135021   .0341903    -0.39   0.693   
>  -.0806257                                            
>               .0536214
       _cons |   .2666667   .0277307     9.62   0.000   
>   .2122248                                            
>               .3211086
--------------------------------------------------------
> ----------------------

          -----------------------------------
                             GraduateDegree 
          -----------------------------------
           Graduate Degree       -0.014     
          -----------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                              -0.039    
     Age                                  0.174    
     Hispanic                            -0.013    
     Black                                0.006    
     HH Income Category                   0.738    
     Voted for Trump (2016 Election)      0.031    
     High School or Less                 -0.008    
     College Degree                       0.027    
     Graduate Degree                     -0.014    
    ------------------------------------------------
                            


Linear regression                               Number o
> f obs     =        856
                                                F(1, 854
> )         =       0.33
                                                Prob > F
>           =     0.5687
                                                R-square
> d         =     0.0004
                                                Root MSE
>           =     .35592

--------------------------------------------------------
> ----------------------
             |               Robust
   knowCovid |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |  -.0145324   .0254858    -0.57   0.569   
>  -.0645545                                            
>               .0354898
       _cons |       .305   .0205294    14.86   0.000   
>    .264706                                            
>                .345294
--------------------------------------------------------
> ----------------------

           ---------------------------------
                                 knowCovid 
           ---------------------------------
            Covid Health Shock    -0.015   
           ---------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                              -0.039    
     Age                                  0.174    
     Hispanic                            -0.013    
     Black                                0.006    
     HH Income Category                   0.738    
     Voted for Trump (2016 Election)      0.031    
     High School or Less                 -0.008    
     College Degree                       0.027    
     Graduate Degree                     -0.014    
     Covid Health Shock                  -0.015    
    ------------------------------------------------
                            


Linear regression                               Number o
> f obs     =        856
                                                F(1, 854
> )         =       0.08
                                                Prob > F
>           =     0.7721
                                                R-square
> d         =     0.0001
                                                Root MSE
>           =     .38022

--------------------------------------------------------
> ----------------------
             |               Robust
jobeffectC~3 |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |  -.0080809   .0278967    -0.29   0.772   
>   -.062835                                            
>               .0466732
       _cons |   .2660141   .0230896    11.52   0.000   
>   .2206951                                            
>               .3113332
--------------------------------------------------------
> ----------------------

         -------------------------------------
                             jobeffectCovid3 
         -------------------------------------
          Employment shock       -0.008      
         -------------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                              -0.039    
     Age                                  0.174    
     Hispanic                            -0.013    
     Black                                0.006    
     HH Income Category                   0.738    
     Voted for Trump (2016 Election)      0.031    
     High School or Less                 -0.008    
     College Degree                       0.027    
     Graduate Degree                     -0.014    
     Covid Health Shock                  -0.015    
     Employment shock                    -0.008    
    ------------------------------------------------
                            


. 
. outreg using "figures_tables/F3_Diff_21.doc", replay(d
> iff) replace

    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                              -0.039    
     Age                                  0.174    
     Hispanic                            -0.013    
     Black                                0.006    
     HH Income Category                   0.738    
     Voted for Trump (2016 Election)      0.031    
     High School or Less                 -0.008    
     College Degree                       0.027    
     Graduate Degree                     -0.014    
     Covid Health Shock                  -0.015    
     Employment shock                    -0.008    
    ------------------------------------------------
                            


. 
. 
. *Differences: (3)-(1)/Anti-elite-Control 
. 
. local i = 1

. 
. foreach var in $DESCVARS {
  2.     reg `var' elite if (elite==1 | control==1), rob
> ust
  3.     outreg, keep(elite)  rtitle("`: var label `var'
> '") stats(b) ///
>         noautosumm store(row`i')  starlevels(10 5 1) s
> tarloc(1)
  4.     outreg, replay(diff2) append(row`i') ctitles(""
> ,Difference ) ///
>         store(diff2) note("")
  5.     local ++i
  6. }

Linear regression                               Number o
> f obs     =        861
                                                F(1, 859
> )         =       0.56
                                                Prob > F
>           =     0.4542
                                                R-square
> d         =     0.0007
                                                Root MSE
>           =     .49947

--------------------------------------------------------
> ----------------------
             |               Robust
      female |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
       elite |   .0267736   .0357558     0.75   0.454   
>  -.0434053                                            
>               .0969525
       _cons |   .5133333   .0288908    17.77   0.000   
>   .4566285                                            
>               .5700382
--------------------------------------------------------
> ----------------------

                   ------------------
                             female 
                   ------------------
                    Female   0.027  
                   ------------------
             * p<0.1; ** p<0.05; *** p<0.01

warning: no table named row1 found with which to merge o
> r append

                -----------------------
                          Difference  
                -----------------------
                 Female      0.027    
                -----------------------
                            


Linear regression                               Number o
> f obs     =        861
                                                F(1, 859
> )         =       0.02
                                                Prob > F
>           =     0.8820
                                                R-square
> d         =     0.0000
                                                Root MSE
>           =     17.272

--------------------------------------------------------
> ----------------------
             |               Robust
         age |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
       elite |   .1848307   1.244829     0.15   0.882   
>  -2.258432                                            
>               2.628094
       _cons |   52.37667   1.013515    51.68   0.000   
>   50.38741                                            
>               54.36592
--------------------------------------------------------
> ----------------------

                     --------------
                             age  
                     --------------
                      Age   0.185 
                     --------------
             * p<0.1; ** p<0.05; *** p<0.01


                -----------------------
                          Difference  
                -----------------------
                 Female      0.027    
                 Age         0.185    
                -----------------------
                            


Linear regression                               Number o
> f obs     =        861
                                                F(1, 859
> )         =       0.36
                                                Prob > F
>           =     0.5494
                                                R-square
> d         =     0.0004
                                                Root MSE
>           =     .26996

--------------------------------------------------------
> ----------------------
             |               Robust
 hispanic_di |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
       elite |  -.0118004   .0197032    -0.60   0.549   
>  -.0504723                                            
>               .0268716
       _cons |   .0866667   .0162624     5.33   0.000   
>   .0547479                                            
>               .1185854
--------------------------------------------------------
> ----------------------

               -------------------------
                           hispanic_di 
               -------------------------
                Hispanic     -0.012    
               -------------------------
             * p<0.1; ** p<0.05; *** p<0.01


               -------------------------
                           Difference  
               -------------------------
                Female        0.027    
                Age           0.185    
                Hispanic     -0.012    
               -------------------------
                            


Linear regression                               Number o
> f obs     =        861
                                                F(1, 859
> )         =       1.85
                                                Prob > F
>           =     0.1743
                                                R-square
> d         =     0.0020
                                                Root MSE
>           =     .28204

--------------------------------------------------------
> ----------------------
             |               Robust
       black |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
       elite |   .0262567   .0193114     1.36   0.174   
>  -.0116464                                            
>               .0641598
       _cons |        .07   .0147481     4.75   0.000   
>   .0410536                                            
>               .0989464
--------------------------------------------------------
> ----------------------

                    ----------------
                             black 
                    ----------------
                     Black   0.026 
                    ----------------
             * p<0.1; ** p<0.05; *** p<0.01


               -------------------------
                           Difference  
               -------------------------
                Female        0.027    
                Age           0.185    
                Hispanic     -0.012    
                Black         0.026    
               -------------------------
                            


Linear regression                               Number o
> f obs     =        861
                                                F(1, 859
> )         =       0.77
                                                Prob > F
>           =     0.3799
                                                R-square
> d         =     0.0009
                                                Root MSE
>           =     7.1561

--------------------------------------------------------
> ----------------------
             |               Robust
         hhi |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
       elite |  -.4444742   .5059003    -0.88   0.380   
>   -1.43742                                            
>               .5484712
       _cons |   12.68333   .4027797    31.49   0.000   
>   11.89279                                            
>               13.47388
--------------------------------------------------------
> ----------------------

             ------------------------------
                                    hhi   
             ------------------------------
              HH Income Category   -0.444 
             ------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


          -----------------------------------
                                Difference  
          -----------------------------------
           Female                  0.027    
           Age                     0.185    
           Hispanic               -0.012    
           Black                   0.026    
           HH Income Category     -0.444    
          -----------------------------------
                            


Linear regression                               Number o
> f obs     =        861
                                                F(1, 859
> )         =       1.68
                                                Prob > F
>           =     0.1949
                                                R-square
> d         =     0.0019
                                                Root MSE
>           =     .49875

--------------------------------------------------------
> ----------------------
             |               Robust
trumpsuppo~r |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
       elite |   .0461676   .0355857     1.30   0.195   
>  -.0236776                                            
>               .1160127
       _cons |   .4333333    .028643    15.13   0.000   
>   .3771148                                            
>               .4895519
--------------------------------------------------------
> ----------------------

  ---------------------------------------------------
                                     trumpsupporter 
  ---------------------------------------------------
   Voted for Trump (2016 Election)       0.046      
  ---------------------------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                               0.027    
     Age                                  0.185    
     Hispanic                            -0.012    
     Black                                0.026    
     HH Income Category                  -0.444    
     Voted for Trump (2016 Election)      0.046    
    ------------------------------------------------
                            


Linear regression                               Number o
> f obs     =        716
                                                F(1, 714
> )         =       0.89
                                                Prob > F
>           =     0.3457
                                                R-square
> d         =     0.0013
                                                Root MSE
>           =      .3622

--------------------------------------------------------
> ----------------------
             |               Robust
HighSchool~s |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
       elite |  -.0272128   .0288387    -0.94   0.346   
>  -.0838316                                            
>               .0294061
       _cons |    .172549   .0236954     7.28   0.000   
>    .126028                                            
>               .2190701
--------------------------------------------------------
> ----------------------

       -----------------------------------------
                              HighSchoolorLess 
       -----------------------------------------
        High School or Less        -0.027      
       -----------------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                               0.027    
     Age                                  0.185    
     Hispanic                            -0.012    
     Black                                0.026    
     HH Income Category                  -0.444    
     Voted for Trump (2016 Election)      0.046    
     High School or Less                 -0.027    
    ------------------------------------------------
                            


Linear regression                               Number o
> f obs     =        716
                                                F(1, 714
> )         =       0.12
                                                Prob > F
>           =     0.7340
                                                R-square
> d         =     0.0002
                                                Root MSE
>           =     .49901

--------------------------------------------------------
> ----------------------
             |               Robust
CollegeDeg~e |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
       elite |  -.0132279   .0389194    -0.34   0.734   
>   -.089638                                            
>               .0631823
       _cons |   .5490196    .031204    17.59   0.000   
>   .4877571                                            
>               .6102821
--------------------------------------------------------
> ----------------------

           ---------------------------------
                             CollegeDegree 
           ---------------------------------
            College Degree      -0.013     
           ---------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                               0.027    
     Age                                  0.185    
     Hispanic                            -0.012    
     Black                                0.026    
     HH Income Category                  -0.444    
     Voted for Trump (2016 Election)      0.046    
     High School or Less                 -0.027    
     College Degree                      -0.013    
    ------------------------------------------------
                            


Linear regression                               Number o
> f obs     =        716
                                                F(1, 714
> )         =       1.31
                                                Prob > F
>           =     0.2521
                                                R-square
> d         =     0.0019
                                                Root MSE
>           =     .42826

--------------------------------------------------------
> ----------------------
             |               Robust
GraduateDe~e |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
       elite |  -.0389009   .0339358    -1.15   0.252   
>  -.1055268                                            
>               .0277249
       _cons |   .2666667   .0277314     9.62   0.000   
>   .2122218                                            
>               .3211116
--------------------------------------------------------
> ----------------------

          -----------------------------------
                             GraduateDegree 
          -----------------------------------
           Graduate Degree       -0.039     
          -----------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                               0.027    
     Age                                  0.185    
     Hispanic                            -0.012    
     Black                                0.026    
     HH Income Category                  -0.444    
     Voted for Trump (2016 Election)      0.046    
     High School or Less                 -0.027    
     College Degree                      -0.013    
     Graduate Degree                     -0.039    
    ------------------------------------------------
                            


Linear regression                               Number o
> f obs     =        861
                                                F(1, 859
> )         =       0.00
                                                Prob > F
>           =     0.9663
                                                R-square
> d         =     0.0000
                                                Root MSE
>           =     .35829

--------------------------------------------------------
> ----------------------
             |               Robust
   knowCovid |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
       elite |  -.0010784   .0255369    -0.04   0.966   
>  -.0512004                                            
>               .0490435
       _cons |       .305   .0205293    14.86   0.000   
>   .2647066                                            
>               .3452934
--------------------------------------------------------
> ----------------------

           ---------------------------------
                                 knowCovid 
           ---------------------------------
            Covid Health Shock    -0.001   
           ---------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                               0.027    
     Age                                  0.185    
     Hispanic                            -0.012    
     Black                                0.026    
     HH Income Category                  -0.444    
     Voted for Trump (2016 Election)      0.046    
     High School or Less                 -0.027    
     College Degree                      -0.013    
     Graduate Degree                     -0.039    
     Covid Health Shock                  -0.001    
    ------------------------------------------------
                            


Linear regression                               Number o
> f obs     =        861
                                                F(1, 859
> )         =       0.07
                                                Prob > F
>           =     0.7942
                                                R-square
> d         =     0.0001
                                                Root MSE
>           =     .36927

--------------------------------------------------------
> ----------------------
             |               Robust
jobeffectC~3 |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
       elite |   -.007163   .0274537    -0.26   0.794   
>  -.0610473                                            
>               .0467212
       _cons |   .2660141   .0230895    11.52   0.000   
>   .2206957                                            
>               .3113325
--------------------------------------------------------
> ----------------------

         -------------------------------------
                             jobeffectCovid3 
         -------------------------------------
          Employment shock       -0.007      
         -------------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                               0.027    
     Age                                  0.185    
     Hispanic                            -0.012    
     Black                                0.026    
     HH Income Category                  -0.444    
     Voted for Trump (2016 Election)      0.046    
     High School or Less                 -0.027    
     College Degree                      -0.013    
     Graduate Degree                     -0.039    
     Covid Health Shock                  -0.001    
     Employment shock                    -0.007    
    ------------------------------------------------
                            


. outreg using "figures_tables/F3_Diff_31.doc", replay(d
> iff2) replace

    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                               0.027    
     Age                                  0.185    
     Hispanic                            -0.012    
     Black                                0.026    
     HH Income Category                  -0.444    
     Voted for Trump (2016 Election)      0.046    
     High School or Less                 -0.027    
     College Degree                      -0.013    
     Graduate Degree                     -0.039    
     Covid Health Shock                  -0.001    
     Employment shock                    -0.007    
    ------------------------------------------------
                            


. 
. 
. * Differences: (4)-(1)/Anti-foreign-Control
. 
. local i = 1

. 
. foreach var in $DESCVARS {
  2.     reg `var' for if (for==1 | control==1), robust
  3.     outreg, keep(for)  rtitle("`: var label `var''"
> ) stats(b) ///
>         noautosumm store(row`i')  starlevels(10 5 1) s
> tarloc(1) 
  4.     outreg, replay(diff3) append(row`i') ctitles(""
> ,Difference ) ///
>         store(diff3) note("")
  5.     local ++i
  6. }

Linear regression                               Number o
> f obs     =        879
                                                F(1, 877
> )         =       0.66
                                                Prob > F
>           =     0.4153
                                                R-square
> d         =     0.0008
                                                Root MSE
>           =     .49933

--------------------------------------------------------
> ----------------------
             |               Robust
      female |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         for |    .028981   .0355571     0.82   0.415   
>  -.0408059                                            
>               .0987679
       _cons |   .5133333   .0288901    17.77   0.000   
>   .4566315                                            
>               .5700352
--------------------------------------------------------
> ----------------------

                   ------------------
                             female 
                   ------------------
                    Female   0.029  
                   ------------------
             * p<0.1; ** p<0.05; *** p<0.01

warning: no table named row1 found with which to merge o
> r append

                -----------------------
                          Difference  
                -----------------------
                 Female      0.029    
                -----------------------
                            


Linear regression                               Number o
> f obs     =        879
                                                F(1, 877
> )         =       0.15
                                                Prob > F
>           =     0.7011
                                                R-square
> d         =     0.0002
                                                Root MSE
>           =     16.852

--------------------------------------------------------
> ----------------------
             |               Robust
         age |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         for |     .46962   1.223125     0.38   0.701   
>  -1.930975                                            
>               2.870215
       _cons |   52.37667   1.013491    51.68   0.000   
>   50.38752                                            
>               54.36582
--------------------------------------------------------
> ----------------------

                     --------------
                             age  
                     --------------
                      Age   0.470 
                     --------------
             * p<0.1; ** p<0.05; *** p<0.01


                -----------------------
                          Difference  
                -----------------------
                 Female      0.029    
                 Age         0.470    
                -----------------------
                            


Linear regression                               Number o
> f obs     =        879
                                                F(1, 877
> )         =       0.00
                                                Prob > F
>           =     0.9876
                                                R-square
> d         =     0.0000
                                                Root MSE
>           =     .28137

--------------------------------------------------------
> ----------------------
             |               Robust
 hispanic_di |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         for |  -.0003109   .0200258    -0.02   0.988   
>  -.0396149                                            
>               .0389931
       _cons |   .0866667    .016262     5.33   0.000   
>   .0547496                                            
>               .1185837
--------------------------------------------------------
> ----------------------

               -------------------------
                           hispanic_di 
               -------------------------
                Hispanic     -0.000    
               -------------------------
             * p<0.1; ** p<0.05; *** p<0.01


               -------------------------
                           Difference  
               -------------------------
                Female        0.029    
                Age           0.470    
                Hispanic     -0.000    
               -------------------------
                            


Linear regression                               Number o
> f obs     =        879
                                                F(1, 877
> )         =       0.17
                                                Prob > F
>           =     0.6763
                                                R-square
> d         =     0.0002
                                                Root MSE
>           =      .2638

--------------------------------------------------------
> ----------------------
             |               Robust
       black |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         for |   .0077202   .0184818     0.42   0.676   
>  -.0285535                                            
>               .0439939
       _cons |        .07   .0147477     4.75   0.000   
>   .0410551                                            
>               .0989449
--------------------------------------------------------
> ----------------------

                    ----------------
                             black 
                    ----------------
                     Black   0.008 
                    ----------------
             * p<0.1; ** p<0.05; *** p<0.01


               -------------------------
                           Difference  
               -------------------------
                Female        0.029    
                Age           0.470    
                Hispanic     -0.000    
                Black         0.008    
               -------------------------
                            


Linear regression                               Number o
> f obs     =        879
                                                F(1, 877
> )         =       0.13
                                                Prob > F
>           =     0.7146
                                                R-square
> d         =     0.0002
                                                Root MSE
>           =     7.0441

--------------------------------------------------------
> ----------------------
             |               Robust
         hhi |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         for |  -.1824698   .4987712    -0.37   0.715   
>  -1.161394                                            
>               .7964548
       _cons |   12.68333   .4027701    31.49   0.000   
>   11.89283                                            
>               13.47384
--------------------------------------------------------
> ----------------------

             ------------------------------
                                    hhi   
             ------------------------------
              HH Income Category   -0.182 
             ------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


          -----------------------------------
                                Difference  
          -----------------------------------
           Female                  0.029    
           Age                     0.470    
           Hispanic               -0.000    
           Black                   0.008    
           HH Income Category     -0.182    
          -----------------------------------
                            


Linear regression                               Number o
> f obs     =        879
                                                F(1, 877
> )         =       5.98
                                                Prob > F
>           =     0.0147
                                                R-square
> d         =     0.0067
                                                Root MSE
>           =     .49879

--------------------------------------------------------
> ----------------------
             |               Robust
trumpsuppo~r |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         for |   .0865285   .0353902     2.44   0.015   
>   .0170691                                            
>               .1559879
       _cons |   .4333333   .0286424    15.13   0.000   
>   .3771177                                            
>               .4895489
--------------------------------------------------------
> ----------------------

  ---------------------------------------------------
                                     trumpsupporter 
  ---------------------------------------------------
   Voted for Trump (2016 Election)      0.087**     
  ---------------------------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                               0.029    
     Age                                  0.470    
     Hispanic                            -0.000    
     Black                                0.008    
     HH Income Category                  -0.182    
     Voted for Trump (2016 Election)     0.087**   
    ------------------------------------------------
                            


Linear regression                               Number o
> f obs     =        751
                                                F(1, 749
> )         =       0.00
                                                Prob > F
>           =     0.9771
                                                R-square
> d         =     0.0000
                                                Root MSE
>           =     .37884

--------------------------------------------------------
> ----------------------
             |               Robust
HighSchool~s |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         for |   .0008381   .0291742     0.03   0.977   
>  -.0564348                                            
>               .0581109
       _cons |    .172549   .0236939     7.28   0.000   
>   .1260347                                            
>               .2190634
--------------------------------------------------------
> ----------------------

       -----------------------------------------
                              HighSchoolorLess 
       -----------------------------------------
        High School or Less        0.001       
       -----------------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                               0.029    
     Age                                  0.470    
     Hispanic                            -0.000    
     Black                                0.008    
     HH Income Category                  -0.182    
     Voted for Trump (2016 Election)     0.087**   
     High School or Less                  0.001    
    ------------------------------------------------
                            


Linear regression                               Number o
> f obs     =        751
                                                F(1, 749
> )         =       0.49
                                                Prob > F
>           =     0.4853
                                                R-square
> d         =     0.0006
                                                Root MSE
>           =     .49952

--------------------------------------------------------
> ----------------------
             |               Robust
CollegeDeg~e |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         for |  -.0268422   .0384441    -0.70   0.485   
>  -.1023131                                            
>               .0486287
       _cons |   .5490196   .0312019    17.60   0.000   
>   .4877659                                            
>               .6102733
--------------------------------------------------------
> ----------------------

           ---------------------------------
                             CollegeDegree 
           ---------------------------------
            College Degree      -0.027     
           ---------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                               0.029    
     Age                                  0.470    
     Hispanic                            -0.000    
     Black                                0.008    
     HH Income Category                  -0.182    
     Voted for Trump (2016 Election)     0.087**   
     High School or Less                  0.001    
     College Degree                      -0.027    
    ------------------------------------------------
                            


Linear regression                               Number o
> f obs     =        751
                                                F(1, 749
> )         =       0.06
                                                Prob > F
>           =     0.8003
                                                R-square
> d         =     0.0001
                                                Root MSE
>           =     .43974

--------------------------------------------------------
> ----------------------
             |               Robust
GraduateDe~e |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         for |  -.0086022   .0339998    -0.25   0.800   
>  -.0753484                                            
>               .0581441
       _cons |   .2666667   .0277296     9.62   0.000   
>   .2122296                                            
>               .3211037
--------------------------------------------------------
> ----------------------

          -----------------------------------
                             GraduateDegree 
          -----------------------------------
           Graduate Degree       -0.009     
          -----------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                               0.029    
     Age                                  0.470    
     Hispanic                            -0.000    
     Black                                0.008    
     HH Income Category                  -0.182    
     Voted for Trump (2016 Election)     0.087**   
     High School or Less                  0.001    
     College Degree                      -0.027    
     Graduate Degree                     -0.009    
    ------------------------------------------------
                            


Linear regression                               Number o
> f obs     =        879
                                                F(1, 877
> )         =       0.03
                                                Prob > F
>           =     0.8614
                                                R-square
> d         =     0.0000
                                                Root MSE
>           =     .36533

--------------------------------------------------------
> ----------------------
             |               Robust
   knowCovid |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         for |  -.0044819   .0256563    -0.17   0.861   
>  -.0548368                                            
>               .0458731
       _cons |       .305   .0205288    14.86   0.000   
>   .2647087                                            
>               .3452913
--------------------------------------------------------
> ----------------------

           ---------------------------------
                                 knowCovid 
           ---------------------------------
            Covid Health Shock    -0.004   
           ---------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                               0.029    
     Age                                  0.470    
     Hispanic                            -0.000    
     Black                                0.008    
     HH Income Category                  -0.182    
     Voted for Trump (2016 Election)     0.087**   
     High School or Less                  0.001    
     College Degree                      -0.027    
     Graduate Degree                     -0.009    
     Covid Health Shock                  -0.004    
    ------------------------------------------------
                            


Linear regression                               Number o
> f obs     =        879
                                                F(1, 877
> )         =       0.44
                                                Prob > F
>           =     0.5082
                                                R-square
> d         =     0.0006
                                                Root MSE
>           =     .36251

--------------------------------------------------------
> ----------------------
             |               Robust
jobeffectC~3 |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         for |  -.0179385   .0271025    -0.66   0.508   
>  -.0711317                                            
>               .0352548
       _cons |   .2660141   .0230889    11.52   0.000   
>   .2206981                                            
>               .3113301
--------------------------------------------------------
> ----------------------

         -------------------------------------
                             jobeffectCovid3 
         -------------------------------------
          Employment shock       -0.018      
         -------------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                               0.029    
     Age                                  0.470    
     Hispanic                            -0.000    
     Black                                0.008    
     HH Income Category                  -0.182    
     Voted for Trump (2016 Election)     0.087**   
     High School or Less                  0.001    
     College Degree                      -0.027    
     Graduate Degree                     -0.009    
     Covid Health Shock                  -0.004    
     Employment shock                    -0.018    
    ------------------------------------------------
                            


. outreg using "figures_tables/F3_Diff_41.doc", replay(d
> iff3) replace

    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                               0.029    
     Age                                  0.470    
     Hispanic                            -0.000    
     Black                                0.008    
     HH Income Category                  -0.182    
     Voted for Trump (2016 Election)     0.087**   
     High School or Less                  0.001    
     College Degree                      -0.027    
     Graduate Degree                     -0.009    
     Covid Health Shock                  -0.004    
     Employment shock                    -0.018    
    ------------------------------------------------
                            


. 
. *Differences: (3)-(4)/Anti-elite-Anti-foreign
. 
. local i = 1

. 
. foreach var in $DESCVARS {
  2.     reg `var' elite if (elite==1 | for==1), robust
  3.     outreg, keep(elite)  rtitle("`: var label `var'
> '") stats(b) ///
>         noautosumm store(row`i')  starlevels(10 5 1) s
> tarloc(1) 
  4.     outreg, replay(diff4) append(row`i') ctitles(""
> ,Difference ) ///
>         store(diff4) note("")
  5.     local ++i
  6. }

Linear regression                               Number o
> f obs     =      1,140
                                                F(1, 113
> 8)        =       0.01
                                                Prob > F
>           =     0.9405
                                                R-square
> d         =     0.0000
                                                Root MSE
>           =     .49873

--------------------------------------------------------
> ----------------------
             |               Robust
      female |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
       elite |  -.0022074   .0295463    -0.07   0.940   
>  -.0601787                                            
>               .0557639
       _cons |   .5423143   .0207229    26.17   0.000   
>   .5016549                                            
>               .5829738
--------------------------------------------------------
> ----------------------

                   ------------------
                             female 
                   ------------------
                    Female   -0.002 
                   ------------------
             * p<0.1; ** p<0.05; *** p<0.01

warning: no table named row1 found with which to merge o
> r append

                -----------------------
                          Difference  
                -----------------------
                 Female     -0.002    
                -----------------------
                            


Linear regression                               Number o
> f obs     =      1,140
                                                F(1, 113
> 8)        =       0.08
                                                Prob > F
>           =     0.7748
                                                R-square
> d         =     0.0001
                                                Root MSE
>           =     16.791

--------------------------------------------------------
> ----------------------
             |               Robust
         age |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
       elite |  -.2847894   .9953471    -0.29   0.775   
>  -2.237711                                            
>               1.668132
       _cons |   52.84629   .6845633    77.20   0.000   
>   51.50314                                            
>               54.18943
--------------------------------------------------------
> ----------------------

                    ---------------
                            age   
                    ---------------
                     Age   -0.285 
                    ---------------
             * p<0.1; ** p<0.05; *** p<0.01


                -----------------------
                          Difference  
                -----------------------
                 Female     -0.002    
                 Age        -0.285    
                -----------------------
                            


Linear regression                               Number o
> f obs     =      1,140
                                                F(1, 113
> 8)        =       0.51
                                                Prob > F
>           =     0.4764
                                                R-square
> d         =     0.0004
                                                Root MSE
>           =     .27256

--------------------------------------------------------
> ----------------------
             |               Robust
 hispanic_di |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
       elite |  -.0114895   .0161302    -0.71   0.476   
>  -.0431378                                            
>               .0201588
       _cons |   .0863558   .0116836     7.39   0.000   
>    .063432                                            
>               .1092796
--------------------------------------------------------
> ----------------------

               -------------------------
                           hispanic_di 
               -------------------------
                Hispanic     -0.011    
               -------------------------
             * p<0.1; ** p<0.05; *** p<0.01


               -------------------------
                           Difference  
               -------------------------
                Female       -0.002    
                Age          -0.285    
                Hispanic     -0.011    
               -------------------------
                            


Linear regression                               Number o
> f obs     =      1,140
                                                F(1, 113
> 8)        =       1.23
                                                Prob > F
>           =     0.2676
                                                R-square
> d         =     0.0011
                                                Root MSE
>           =      .2817

--------------------------------------------------------
> ----------------------
             |               Robust
       black |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
       elite |   .0185365   .0167139     1.11   0.268   
>   -.014257                                            
>                 .05133
       _cons |   .0777202   .0111363     6.98   0.000   
>   .0558703                                            
>               .0995702
--------------------------------------------------------
> ----------------------

                    ----------------
                             black 
                    ----------------
                     Black   0.019 
                    ----------------
             * p<0.1; ** p<0.05; *** p<0.01


               -------------------------
                           Difference  
               -------------------------
                Female       -0.002    
                Age          -0.285    
                Hispanic     -0.011    
                Black         0.019    
               -------------------------
                            


Linear regression                               Number o
> f obs     =      1,140
                                                F(1, 113
> 8)        =       0.38
                                                Prob > F
>           =     0.5372
                                                R-square
> d         =     0.0003
                                                Root MSE
>           =     7.1619

--------------------------------------------------------
> ----------------------
             |               Robust
         hhi |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
       elite |  -.2620044   .4244456    -0.62   0.537   
>  -1.094788                                            
>               .5707794
       _cons |   12.50086   .2941153    42.50   0.000   
>   11.92379                                            
>               13.07793
--------------------------------------------------------
> ----------------------

             ------------------------------
                                    hhi   
             ------------------------------
              HH Income Category   -0.262 
             ------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


          -----------------------------------
                                Difference  
          -----------------------------------
           Female                 -0.002    
           Age                    -0.285    
           Hispanic               -0.011    
           Black                   0.019    
           HH Income Category     -0.262    
          -----------------------------------
                            


Linear regression                               Number o
> f obs     =      1,140
                                                F(1, 113
> 8)        =       1.86
                                                Prob > F
>           =     0.1733
                                                R-square
> d         =     0.0016
                                                Root MSE
>           =     .50003

--------------------------------------------------------
> ----------------------
             |               Robust
trumpsuppo~r |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
       elite |  -.0403609    .029623    -1.36   0.173   
>  -.0984827                                            
>               .0177608
       _cons |   .5198618   .0207811    25.02   0.000   
>   .4790882                                            
>               .5606355
--------------------------------------------------------
> ----------------------

  ---------------------------------------------------
                                     trumpsupporter 
  ---------------------------------------------------
   Voted for Trump (2016 Election)       -0.040     
  ---------------------------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                              -0.002    
     Age                                 -0.285    
     Hispanic                            -0.011    
     Black                                0.019    
     HH Income Category                  -0.262    
     Voted for Trump (2016 Election)     -0.040    
    ------------------------------------------------
                            


Linear regression                               Number o
> f obs     =        957
                                                F(1, 955
> )         =       1.41
                                                Prob > F
>           =     0.2360
                                                R-square
> d         =     0.0015
                                                Root MSE
>           =      .3666

--------------------------------------------------------
> ----------------------
             |               Robust
HighSchool~s |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
       elite |  -.0280509   .0236553    -1.19   0.236   
>  -.0744732                                            
>               .0183715
       _cons |   .1733871   .0170166    10.19   0.000   
>   .1399928                                            
>               .2067814
--------------------------------------------------------
> ----------------------

       -----------------------------------------
                              HighSchoolorLess 
       -----------------------------------------
        High School or Less        -0.028      
       -----------------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                              -0.002    
     Age                                 -0.285    
     Hispanic                            -0.011    
     Black                                0.019    
     HH Income Category                  -0.262    
     Voted for Trump (2016 Election)     -0.040    
     High School or Less                 -0.028    
    ------------------------------------------------
                            


Linear regression                               Number o
> f obs     =        957
                                                F(1, 955
> )         =       0.18
                                                Prob > F
>           =     0.6737
                                                R-square
> d         =     0.0002
                                                Root MSE
>           =     .49965

--------------------------------------------------------
> ----------------------
             |               Robust
CollegeDeg~e |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
       elite |   .0136143   .0323225     0.42   0.674   
>   -.049817                                            
>               .0770457
       _cons |   .5221774    .022452    23.26   0.000   
>   .4781164                                            
>               .5662385
--------------------------------------------------------
> ----------------------

           ---------------------------------
                             CollegeDegree 
           ---------------------------------
            College Degree       0.014     
           ---------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                              -0.002    
     Age                                 -0.285    
     Hispanic                            -0.011    
     Black                                0.019    
     HH Income Category                  -0.262    
     Voted for Trump (2016 Election)     -0.040    
     High School or Less                 -0.028    
     College Degree                       0.014    
    ------------------------------------------------
                            


Linear regression                               Number o
> f obs     =        957
                                                F(1, 955
> )         =       1.19
                                                Prob > F
>           =     0.2749
                                                R-square
> d         =     0.0012
                                                Root MSE
>           =     .42936

--------------------------------------------------------
> ----------------------
             |               Robust
GraduateDe~e |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
       elite |  -.0302988   .0277339    -1.09   0.275   
>  -.0847251                                            
>               .0241276
       _cons |   .2580645    .019668    13.12   0.000   
>    .219467                                            
>                .296662
--------------------------------------------------------
> ----------------------

          -----------------------------------
                             GraduateDegree 
          -----------------------------------
           Graduate Degree       -0.030     
          -----------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                              -0.002    
     Age                                 -0.285    
     Hispanic                            -0.011    
     Black                                0.019    
     HH Income Category                  -0.262    
     Voted for Trump (2016 Election)     -0.040    
     High School or Less                 -0.028    
     College Degree                       0.014    
     Graduate Degree                     -0.030    
    ------------------------------------------------
                            


Linear regression                               Number o
> f obs     =      1,140
                                                F(1, 113
> 8)        =       0.02
                                                Prob > F
>           =     0.8749
                                                R-square
> d         =     0.0000
                                                Root MSE
>           =     .36504

--------------------------------------------------------
> ----------------------
             |               Robust
   knowCovid |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
       elite |   .0034034   .0216158     0.16   0.875   
>  -.0390078                                            
>               .0458147
       _cons |   .3005181   .0153848    19.53   0.000   
>   .2703323                                            
>               .3307039
--------------------------------------------------------
> ----------------------

           ---------------------------------
                                 knowCovid 
           ---------------------------------
            Covid Health Shock     0.003   
           ---------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                              -0.002    
     Age                                 -0.285    
     Hispanic                            -0.011    
     Black                                0.019    
     HH Income Category                  -0.262    
     Voted for Trump (2016 Election)     -0.040    
     High School or Less                 -0.028    
     College Degree                       0.014    
     Graduate Degree                     -0.030    
     Covid Health Shock                   0.003    
    ------------------------------------------------
                            


Linear regression                               Number o
> f obs     =      1,140
                                                F(1, 113
> 8)        =       0.28
                                                Prob > F
>           =     0.5999
                                                R-square
> d         =     0.0002
                                                Root MSE
>           =     .34651

--------------------------------------------------------
> ----------------------
             |               Robust
jobeffectC~3 |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
       elite |   .0107754   .0205377     0.52   0.600   
>  -.0295205                                            
>               .0510714
       _cons |   .2480757   .0141894    17.48   0.000   
>   .2202353                                            
>                .275916
--------------------------------------------------------
> ----------------------

         -------------------------------------
                             jobeffectCovid3 
         -------------------------------------
          Employment shock        0.011      
         -------------------------------------
             * p<0.1; ** p<0.05; *** p<0.01


    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                              -0.002    
     Age                                 -0.285    
     Hispanic                            -0.011    
     Black                                0.019    
     HH Income Category                  -0.262    
     Voted for Trump (2016 Election)     -0.040    
     High School or Less                 -0.028    
     College Degree                       0.014    
     Graduate Degree                     -0.030    
     Covid Health Shock                   0.003    
     Employment shock                     0.011    
    ------------------------------------------------
                            


. outreg using "figures_tables/F3_Diff_34.doc", replay(d
> iff4) replace

    ------------------------------------------------
                                       Difference  
    ------------------------------------------------
     Female                              -0.002    
     Age                                 -0.285    
     Hispanic                            -0.011    
     Black                                0.019    
     HH Income Category                  -0.262    
     Voted for Trump (2016 Election)     -0.040    
     High School or Less                 -0.028    
     College Degree                       0.014    
     Graduate Degree                     -0.030    
     Covid Health Shock                   0.003    
     Employment shock                     0.011    
    ------------------------------------------------
                            


. 
. 
. local count: word count $DESCVARS

. mat sumstat = J(`count',8,.)

. 
. local i = 1

. foreach var in $DESCVARS {
  2.         quietly: summarize `var' if control==1
  3.         mat sumstat[`i',1] = r(mean)
  4.         mat sumstat[`i',2] = r(sd)
  5.     quietly: summarize `var' if elite==0 & for==0 &
>  control==0
  6.     mat sumstat[`i',3] = r(mean)
  7.     mat sumstat[`i',4] = r(sd)
  8.     quietly: summarize `var' if elite==1  
  9.     mat sumstat[`i',5] = r(mean)
 10.     mat sumstat[`i',6] = r(sd)
 11.     quietly: summarize `var' if for==1  
 12.     mat sumstat[`i',7] = r(mean)
 13.     mat sumstat[`i',8] = r(sd)
 14.     local i = `i' + 1
 15. }

. 
. frmttable, statmat(sumstat) store(sumstat) sfmt(f,f,f,
> f,f,f,f,f)

--------------------------------------------------------
 0.51   0.50   0.47   0.50   0.54   0.50   0.54   0.50  
 52.38  17.56  52.55  16.36  52.56  17.11  52.85  16.47 
 0.09   0.28   0.07   0.26   0.07   0.26   0.09   0.28  
 0.07   0.26   0.08   0.26   0.10   0.30   0.08   0.27  
 12.68  6.98   13.42  7.01   12.24  7.25   12.50  7.08  
 0.43   0.50   0.46   0.50   0.48   0.50   0.52   0.50  
 0.17   0.38   0.16   0.37   0.15   0.35   0.17   0.38  
 0.55   0.50   0.58   0.49   0.54   0.50   0.52   0.50  
 0.27   0.44   0.25   0.44   0.23   0.42   0.26   0.44  
 0.30   0.36   0.29   0.36   0.30   0.36   0.30   0.37  
 0.27   0.40   0.26   0.37   0.26   0.35   0.25   0.34  
--------------------------------------------------------


. 
. 
. outreg using "figures_tables/F3balance.doc", ///
>     replay(sumstat) nocenter note("") plain replace //
> /
>     ctitles( "", "", "", "Technocratic", "", "Antielit
> e", "", "Antiforeign", "" \ "", mean, sd,  mean, sd,  
> mean, sd, mean, sd, Diff) ///
>         multicol(1,2,2;1,4,2; 1,6,2; 1,8,2) 
warning: matrix in ctitles option has varying size rows:
    "", "", "", "Technocratic", "", "Antielite", "", "An
> tiforeign", "" \ "", mean, sd,  mean, sd,  mean, sd, m
> ean, sd, Diff

   --------------------------------------------------
                     Technocratic         Antielite 
       mean    sd        mean       sd      mean    
   --------------------------------------------------
       0.51   0.50       0.47      0.50     0.54    
       52.38  17.56     52.55      16.36    52.56   
       0.09   0.28       0.07      0.26     0.07    
       0.07   0.26       0.08      0.26     0.10    
       12.68  6.98      13.42      7.01     12.24   
       0.43   0.50       0.46      0.50     0.48    
       0.17   0.38       0.16      0.37     0.15    
       0.55   0.50       0.58      0.49     0.54    
       0.27   0.44       0.25      0.44     0.23    
       0.30   0.36       0.29      0.36     0.30    
       0.27   0.40       0.26      0.37     0.26    
   --------------------------------------------------
                            


         -------------------------------------
                    Antiforeign              
              sd       mean       sd    Diff 
         -------------------------------------
             0.50      0.54      0.50        
             17.11     52.85     16.47       
             0.26      0.09      0.28        
             0.30      0.08      0.27        
             7.25      12.50     7.08        
             0.50      0.52      0.50        
             0.35      0.17      0.38        
             0.50      0.52      0.50        
             0.42      0.26      0.44        
             0.36      0.30      0.37        
             0.35      0.25      0.34        
         -------------------------------------
                            


. 
. 
. *************************************************     
>  
. *FIGURE F1: ALTERNATIVE ECONOMIC RISK CATEGORIES*
. *************************************************
. 
. set scheme plotplain

. 
. preserve

. 
. collapse (mean) meansupport= pandemicsupport (sd) sdsu
> pport=pandemicsupport (count) n=pandemicsupport, by(ec
> onshock2 treatment)

. 
. generate hisupport = meansupport + invttail(n-1,0.025)
> *(sdsupport / sqrt(n))

. generate lowsupport = meansupport - invttail(n-1,0.025
> )*(sdsupport / sqrt(n))

. 
. gen econtreatment = treatment+1 if econshock2==0
(8 missing values generated)

. replace econtreatment = treatment+6 if econshock2==1
(4 real changes made)

. replace econtreatment = treatment+11 if econshock2==2
(4 real changes made)

. 
. twoway (bar meansupport econtreatment if treatment==0)
>  ///
>        (bar meansupport econtreatment if treatment==1)
>  ///
>        (bar meansupport econtreatment if treatment==2)
>  ///
>        (bar meansupport econtreatment if treatment==3)
>  ///
>        (rcap hisupport lowsupport econtreatment), ///
>            yscale(range(1 4)) ylabel(1 (1) 4) ///
>            xlabel( 2.5 "Low" 7.5 "Middle" 12.5 "High",
>  noticks) ///
>            xtitle("Economic Risk") ytitle("Mean Suppor
> t Redistribution") ///
>        legend( order(1 "Control" 2 "Technocratic" 3 "A
> nti-Foreign" 4 "Anti-Elite") )

. graph export "figures_tables/FigureF1.tif", replace
(file figures_tables/FigureF1.tif written in TIFF format
> )

.            
. restore

. 
. 
. *********************************
. *FIGURE F2: DISTRIBUTION OF RISK*
. *********************************
. 
. graph box jobeffectCovid3, over(treatment) ytitle("Eco
> nomic Risk")

. graph export "figures_tables/FigureF2.tif", replace
(file figures_tables/FigureF2.tif written in TIFF format
> )

. 
. 
. *******************************
. *TABLE F4: DICHOTIMOUS RESULTS*
. *******************************
. 
. eststo m9Aa: reg pandemicsupport_di knowCovid tech for
>  elite, robust

Linear regression                               Number o
> f obs     =      1,995
                                                F(4, 199
> 0)        =       0.70
                                                Prob > F
>           =     0.5907
                                                R-square
> d         =     0.0015
                                                Root MSE
>           =     .43418

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~i |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
   knowCovid |  -.0135201   .0276595    -0.49   0.625   
>  -.0677648                                            
>               .0407246
        tech |   .0093239   .0305852     0.30   0.761   
>  -.0506586                                            
>               .0693064
         for |   -.007159   .0306348    -0.23   0.815   
>  -.0672387                                            
>               .0529206
       elite |  -.0316739   .0311418    -1.02   0.309   
>  -.0927478                                            
>                  .0294
       _cons |   .7607903   .0256974    29.61   0.000   
>   .7103937                                            
>               .8111869
--------------------------------------------------------
> ----------------------

. eststo m9Ab: reg pandemicsupport_di knowCovid tech for
>  elite techXknow forXknow eliteXknow, robust

Linear regression                               Number o
> f obs     =      1,995
                                                F(7, 198
> 7)        =       2.04
                                                Prob > F
>           =     0.0475
                                                R-square
> d         =     0.0077
                                                Root MSE
>           =     .43315

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~i |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
   knowCovid |  -.1647618   .0760446    -2.17   0.030   
>  -.3138974                                            
>              -.0156263
        tech |   -.058327   .0399465    -1.46   0.144   
>  -.1366683                                            
>               .0200144
         for |  -.0785026   .0398763    -1.97   0.049   
>  -.1567064                                            
>              -.0002987
       elite |  -.0519835    .040225    -1.29   0.196   
>   -.130871                                            
>               .0269041
   techXknow |   .2253365   .0912128     2.47   0.014   
>   .0464537                                            
>               .4042193
    forXknow |   .2351461   .0895397     2.63   0.009   
>   .0595445                                            
>               .4107477
  eliteXknow |   .0664398   .0936885     0.71   0.478   
>  -.1172982                                            
>               .2501779
       _cons |    .806919   .0320303    25.19   0.000   
>   .7441025                                            
>               .8697356
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (elite + eliteXknow)

 ( 1)  for - elite + forXknow - eliteXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~i |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .1421872   .0567992     2.50   0.012   
>   .0307949                                            
>               .2535795
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (tech + techXknow)

 ( 1)  - tech + for - techXknow + forXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~i |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   -.010366   .0533688    -0.19   0.846   
>  -.1150306                                            
>               .0942987
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXknow) - (tech + techXknow)

 ( 1)  - tech + elite - techXknow + eliteXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~i |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.1525532   .0584477    -2.61   0.009   
>  -.2671783                                            
>              -.0379281
--------------------------------------------------------
> ----------------------

. eststo m9Ac: reg pandemicsupport_di jobeffectCovid3 te
> ch for elite, robust

Linear regression                               Number o
> f obs     =      1,995
                                                F(4, 199
> 0)        =       0.66
                                                Prob > F
>           =     0.6225
                                                R-square
> d         =     0.0013
                                                Root MSE
>           =      .4342

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~i |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
jobeffectC~3 |  -.0001621   .0278736    -0.01   0.995   
>  -.0548267                                            
>               .0545025
        tech |   .0095191   .0306278     0.31   0.756   
>  -.0505468                                            
>               .0695849
         for |  -.0071014   .0306519    -0.23   0.817   
>  -.0672146                                            
>               .0530119
       elite |  -.0316678   .0311788    -1.02   0.310   
>  -.0928143                                            
>               .0294788
       _cons |   .7567098    .025734    29.41   0.000   
>   .7062415                                            
>               .8071781
--------------------------------------------------------
> ----------------------

. eststo m9Ad: reg pandemicsupport_di jobeffectCovid3 te
> ch for elite techXjobeffectCovid3 forXjobeffectCovid3 
> eliteXjobeffectCovid3, robust

Linear regression                               Number o
> f obs     =      1,995
                                                F(7, 198
> 7)        =       0.71
                                                Prob > F
>           =     0.6613
                                                R-square
> d         =     0.0025
                                                Root MSE
>           =     .43429

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~i |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
jobeffectC~3 |  -.0478634   .0723015    -0.66   0.508   
>  -.1896582                                            
>               .0939314
        tech |  -.0051503   .0379529    -0.14   0.892   
>  -.0795821                                            
>               .0692814
         for |  -.0343346   .0382565    -0.90   0.370   
>  -.1093617                                            
>               .0406925
       elite |  -.0359355   .0391805    -0.92   0.359   
>  -.1127748                                            
>               .0409037
techXjobef~3 |   .0553784   .0853264     0.65   0.516   
>  -.1119603                                            
>               .2227171
forXjobeff~3 |   .1063288    .085996     1.24   0.216   
>  -.0623229                                            
>               .2749805
eliteXjobe~3 |   .0152254   .0916226     0.17   0.868   
>  -.1644611                                            
>               .1949119
       _cons |    .769399   .0312421    24.63   0.000   
>   .7081283                                            
>               .8306697
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (elite + eliteXjo
> beffectCovid3)

 ( 1)  for - elite + forXjobeffectCovid3 -
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~i |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .0927043   .0599036     1.55   0.122   
>  -.0247762                                            
>               .2101847
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (tech + techXjobe
> ffectCovid3)

 ( 1)  - tech + for - techXjobeffectCovid3 +
       forXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~i |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .0217661   .0538843     0.40   0.686   
>  -.0839095                                            
>               .1274417
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXjobeffectCovid3) - (tech + techX
> jobeffectCovid3)

 ( 1)  - tech + elite - techXjobeffectCovid3 +
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~i |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.0709382    .059736    -1.19   0.235   
>    -.18809                                            
>               .0462136
--------------------------------------------------------
> ----------------------

. 
. esttab m9Aa m9Ab m9Ac m9Ad using "figures_tables/Appen
> dixtableF4A.rtf", order(for elite tech knowCovid forXk
> now eliteXknow techXknow jobeffectCovid3 forXjobeffect
> Covid3 eliteXjobeffectCovid3 techXjobeffectCovid3) $es
> ttabformat replace label onecell
(output written to figures_tables/AppendixtableF4A.rtf)

. 
. 
. eststo m9Ba: reg pandemicsupport_di2 knowCovid tech fo
> r elite, robust

Linear regression                               Number o
> f obs     =      1,995
                                                F(4, 199
> 0)        =       2.12
                                                Prob > F
>           =     0.0761
                                                R-square
> d         =     0.0041
                                                Root MSE
>           =     .48423

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~2 |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
   knowCovid |   .0500154   .0299992     1.67   0.096   
>  -.0088177                                            
>               .1088484
        tech |    .039264   .0345073     1.14   0.255   
>  -.0284102                                            
>               .1069383
         for |  -.0151644   .0346445    -0.44   0.662   
>  -.0831078                                            
>               .0527789
       elite |  -.0233065   .0349373    -0.67   0.505   
>   -.091824                                            
>               .0452109
       _cons |   .6080786   .0293852    20.69   0.000   
>   .5504497                                            
>               .6657076
--------------------------------------------------------
> ----------------------

. eststo m9Bb: reg pandemicsupport_di2 knowCovid tech fo
> r elite techXknow forXknow eliteXknow, robust

Linear regression                               Number o
> f obs     =      1,995
                                                F(7, 198
> 7)        =       2.48
                                                Prob > F
>           =     0.0156
                                                R-square
> d         =     0.0082
                                                Root MSE
>           =      .4836

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~2 |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
   knowCovid |  -.1066261   .0805738    -1.32   0.186   
>  -.2646441                                            
>               .0513918
        tech |  -.0274655   .0453768    -0.61   0.545   
>  -.1164566                                            
>               .0615256
         for |  -.0847777   .0453808    -1.87   0.062   
>  -.1737767                                            
>               .0042212
       elite |  -.0524872   .0458414    -1.14   0.252   
>  -.1423895                                            
>                .037415
   techXknow |   .2218945   .0976634     2.27   0.023   
>   .0303611                                            
>               .4134278
    forXknow |   .2293081   .0965434     2.38   0.018   
>   .0399712                                            
>               .4186449
  eliteXknow |   .0955671   .0993497     0.96   0.336   
>  -.0992733                                            
>               .2904075
       _cons |   .6558543   .0368785    17.78   0.000   
>   .5835298                                            
>               .7281788
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (elite + eliteXknow)

 ( 1)  for - elite + forXknow - eliteXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~2 |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .1014505   .0615075     1.65   0.099   
>  -.0191754                                            
>               .2220764
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (tech + techXknow)

 ( 1)  - tech + for - techXknow + forXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~2 |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.0498986     .05957    -0.84   0.402   
>  -.1667249                                            
>               .0669277
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXknow) - (tech + techXknow)

 ( 1)  - tech + elite - techXknow + eliteXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~2 |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.1513491   .0624797    -2.42   0.016   
>  -.2738817                                            
>              -.0288165
--------------------------------------------------------
> ----------------------

. eststo m9Bc: reg pandemicsupport_di2 jobeffectCovid3 t
> ech for elite, robust

Linear regression                               Number o
> f obs     =      1,995
                                                F(4, 199
> 0)        =       2.86
                                                Prob > F
>           =     0.0223
                                                R-square
> d         =     0.0057
                                                Root MSE
>           =     .48385

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~2 |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
jobeffectC~3 |   .0730182    .030492     2.39   0.017   
>   .0132187                                            
>               .1328178
        tech |   .0391272   .0344479     1.14   0.256   
>  -.0284305                                            
>                .106685
         for |  -.0140788   .0345498    -0.41   0.684   
>  -.0818364                                            
>               .0536789
       elite |  -.0228441   .0348474    -0.66   0.512   
>  -.0911852                                            
>               .0454971
       _cons |   .6039094   .0290361    20.80   0.000   
>   .5469651                                            
>               .6608538
--------------------------------------------------------
> ----------------------

. eststo m9Bd: reg pandemicsupport_di2 jobeffectCovid3 t
> ech for elite techXjobeffectCovid3 forXjobeffectCovid3
>  eliteXjobeffectCovid3, robust

Linear regression                               Number o
> f obs     =      1,995
                                                F(7, 198
> 7)        =       2.07
                                                Prob > F
>           =     0.0441
                                                R-square
> d         =     0.0067
                                                Root MSE
>           =     .48397

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~2 |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
jobeffectC~3 |   .0334042   .0810023     0.41   0.680   
>  -.1254541                                            
>               .1922625
        tech |   .0289196   .0427226     0.68   0.499   
>  -.0548663                                            
>               .1127054
         for |  -.0412775   .0433929    -0.95   0.342   
>  -.1263778                                            
>               .0438228
       elite |  -.0238926   .0437948    -0.55   0.585   
>  -.1097813                                            
>                .061996
techXjobef~3 |   .0383336    .093943     0.41   0.683   
>  -.1459035                                            
>               .2225708
forXjobeff~3 |   .1067744   .0993305     1.07   0.283   
>  -.0880285                                            
>               .3015772
eliteXjobe~3 |     .00302   .1007925     0.03   0.976   
>  -.1946501                                            
>               .2006902
       _cons |   .6144473   .0352344    17.44   0.000   
>   .5453472                                            
>               .6835475
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (elite + eliteXjo
> beffectCovid3)

 ( 1)  for - elite + forXjobeffectCovid3 -
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~2 |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .0863695   .0676879     1.28   0.202   
>  -.0463772                                            
>               .2191161
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (tech + techXjobe
> ffectCovid3)

 ( 1)  - tech + for - techXjobeffectCovid3 +
       forXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~2 |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.0017564   .0612652    -0.03   0.977   
>  -.1219071                                            
>               .1183944
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXjobeffectCovid3) - (tech + techX
> jobeffectCovid3)

 ( 1)  - tech + elite - techXjobeffectCovid3 +
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~2 |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.0881258   .0628736    -1.40   0.161   
>   -.211431                                            
>               .0351794
--------------------------------------------------------
> ----------------------

. 
. esttab m9Ba m9Bb m9Bc m9Bd using "figures_tables/Appen
> dixtableF4B.rtf", order(for elite tech knowCovid forXk
> now eliteXknow techXknow jobeffectCovid3 forXjobeffect
> Covid3 eliteXjobeffectCovid3 techXjobeffectCovid3) $es
> ttabformat replace label onecell
(output written to figures_tables/AppendixtableF4B.rtf)

. 
. *note lincom estimates added manually*
. 
. ******************************************************
> ****
. *FIGURE F3: BAR CHART: DICHOTOMOUS RESULTS BY HEALTH R
> ISK*
. ******************************************************
> ****
. 
. set scheme plotplain

. 
. preserve

. 
. collapse (mean) meansupport= pandemicsupport_di (sd) s
> dsupport=pandemicsupport_di (count) n=pandemicsupport_
> di, by(healthshock treatment)

. 
. generate hisupport = meansupport + invttail(n-1,0.025)
> *(sdsupport / sqrt(n))

. generate lowsupport = meansupport - invttail(n-1,0.025
> )*(sdsupport / sqrt(n))

. 
. gen healthtreatment = treatment+1 if healthshock==0
(8 missing values generated)

. replace healthtreatment = treatment+6 if healthshock==
> 1
(4 real changes made)

. replace healthtreatment = treatment+11 if healthshock=
> =2
(4 real changes made)

. 
. twoway (bar meansupport healthtreatment if treatment==
> 0) ///
>        (bar meansupport healthtreatment if treatment==
> 1) ///
>        (bar meansupport healthtreatment if treatment==
> 2) ///
>        (bar meansupport healthtreatment if treatment==
> 3) ///
>        (rcap hisupport lowsupport healthtreatment), //
> /
>            yscale(range(0 1)) ylabel(0 (.2) 1) ///
>            xlabel( 2.5 "Low" 7.5 "Middle" 12.5 "High",
>  noticks) ///
>            xtitle("Health Risk") ytitle("Prop. Support
>  Redistribution") ///
>        legend( order(1 "Control" 2 "Technocratic" 3 "A
> nti-Foreign" 4 "Anti-Elite") )

. graph export "figures_tables/FigureF3.tif", replace
(file figures_tables/FigureF3.tif written in TIFF format
> )

. 
. restore

. 
. 
. ******************************************************
> **
. *FIGURE F4: BAR CHART: DICHOTOMOUS RESULTS BY ECON RIS
> K*
. ******************************************************
> **
. 
. preserve

. 
. collapse (mean) meansupport= pandemicsupport_di (sd) s
> dsupport=pandemicsupport_di (count) n=pandemicsupport_
> di, by(econshock treatment)

. 
. generate hisupport = meansupport + invttail(n-1,0.025)
> *(sdsupport / sqrt(n))

. generate lowsupport = meansupport - invttail(n-1,0.025
> )*(sdsupport / sqrt(n))

. 
. gen econtreatment = treatment+1 if econshock==0
(8 missing values generated)

. replace econtreatment = treatment+6 if econshock==1
(4 real changes made)

. replace econtreatment = treatment+11 if econshock==2
(4 real changes made)

. 
. twoway (bar meansupport econtreatment if treatment==0)
>  ///
>        (bar meansupport econtreatment if treatment==1)
>  ///
>        (bar meansupport econtreatment if treatment==2)
>  ///
>        (bar meansupport econtreatment if treatment==3)
>  ///
>        (rcap hisupport lowsupport econtreatment), ///
>            yscale(range(0 1)) ylabel(0 (.2) 1) ///
>            xlabel( 2.5 "Low" 7.5 "Middle" 12.5 "High",
>  noticks) ///
>            xtitle("Economic Risk") ytitle("Prop. Suppo
> rt Redistribution") ///
>        legend( order(1 "Control" 2 "Technocratic" 3 "A
> nti-Foreign" 4 "Anti-Elite") )

. graph export "figures_tables/FigureF4.tif", replace
(file figures_tables/FigureF4.tif written in TIFF format
> )

.            
. restore

. 
. 
. ******************************************
. *TABLE F5: COVID EXPOSURE WITHOUT FRIENDS*
. ******************************************
. 
. eststo mA6: reg pandemicsupport knowCovid_nofriends te
> ch for elite techXknow_nofriends forXknow_nofriends el
> iteXknow_nofriends, robust

Linear regression                               Number o
> f obs     =      1,995
                                                F(7, 198
> 7)        =       3.62
                                                Prob > F
>           =     0.0007
                                                R-square
> d         =     0.0143
                                                Root MSE
>           =     1.0275

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
knowCovid_~s |  -.4239806   .1974491    -2.15   0.032   
>  -.8112096                                            
>              -.0367517
        tech |   -.032111   .0867301    -0.37   0.711   
>  -.2022023                                            
>               .1379804
         for |  -.1785175   .0848077    -2.10   0.035   
>  -.3448389                                            
>              -.0121961
       elite |  -.1173045   .0859163    -1.37   0.172   
>  -.2857999                                            
>                .051191
techXknow_~s |   .5679008   .2409936     2.36   0.019   
>   .0952741                                            
>               1.040528
forXknow_n~s |    .883558   .2293653     3.85   0.000   
>   .4337364                                            
>                1.33338
eliteXknow~s |   .3634264     .24048     1.51   0.131   
>   -.108193                                            
>               .8350457
       _cons |   3.553602   .0700803    50.71   0.000   
>   3.416164                                            
>               3.691041
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (elite + eliteXknow)

 ( 1)  for - elite + forXknow_nofriends -
       eliteXknow_nofriends = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .4589186   .1570129     2.92   0.004   
>   .1509914                                            
>               .7668458
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (tech + techXknow)

 ( 1)  - tech + for - techXknow_nofriends +
       forXknow_nofriends = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .1692507   .1578872     1.07   0.284   
>  -.1403911                                            
>               .4788925
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXknow) - (tech + techXknow)

 ( 1)  - tech + elite - techXknow_nofriends +
       eliteXknow_nofriends = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   -.289668   .1710103    -1.69   0.090   
>  -.6250463                                            
>               .0457103
--------------------------------------------------------
> ----------------------

. 
. esttab mA6 using "figures_tables/AppendixtableF5.rtf",
>  order(for elite tech knowCovid_nofriends forXknow_nof
> riends eliteXknow_nofriends techXknow_nofriends) $estt
> abformat replace label onecell
(output written to figures_tables/AppendixtableF5.rtf)

. *note: lincom estimates added manually*
. 
. 
. *****************************************
. *TABLE F6A: EFFECTS FOR TRUMP SUPPORTERS*
. *****************************************
. 
. eststo mA5Aa: reg pandemicsupport tech for elite techX
> know forXknow eliteXknow knowCovid if trumpsupporter==
> 1, robust

Linear regression                               Number o
> f obs     =        958
                                                F(7, 950
> )         =       7.83
                                                Prob > F
>           =     0.0000
                                                R-square
> d         =     0.0571
                                                Root MSE
>           =     1.0429

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |  -.1544861   .1627792    -0.95   0.343   
>  -.4739344                                            
>               .1649623
         for |  -.1803261   .1523982    -1.18   0.237   
>  -.4794021                                            
>               .1187498
       elite |  -.0794946   .1583451    -0.50   0.616   
>  -.3902411                                            
>               .2312519
   techXknow |   .8959491   .3314808     2.70   0.007   
>   .2454298                                            
>               1.546468
    forXknow |    1.01404   .3211921     3.16   0.002   
>    .383712                                            
>               1.644368
  eliteXknow |   .3968656   .3372625     1.18   0.240   
>  -.2649999                                            
>               1.058731
   knowCovid |  -.1664755   .2838841    -0.59   0.558   
>  -.7235879                                            
>                .390637
       _cons |   3.196085   .1354568    23.59   0.000   
>   2.930256                                            
>               3.461915
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (elite + eliteXknow)

 ( 1)  for - elite + forXknow - eliteXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .5163428   .1895259     2.72   0.007   
>    .144405                                            
>               .8882806
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (tech + techXknow)

 ( 1)  - tech + for - techXknow + forXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .0922508   .1764693     0.52   0.601   
>   -.254064                                            
>               .4385656
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXknow) - (tech + techXknow)

 ( 1)  - tech + elite - techXknow + eliteXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   -.424092   .1919678    -2.21   0.027   
>   -.800822                                            
>               -.047362
--------------------------------------------------------
> ----------------------

. test for + forXknow=elite + eliteXknow

 ( 1)  for - elite + forXknow - eliteXknow = 0

       F(  1,   950) =    7.42
            Prob > F =    0.0066

. test for + forXknow = tech + techXknow

 ( 1)  - tech + for - techXknow + forXknow = 0

       F(  1,   950) =    0.27
            Prob > F =    0.6013

. test elite + eliteXknow = tech + techXknow

 ( 1)  - tech + elite - techXknow + eliteXknow = 0

       F(  1,   950) =    4.88
            Prob > F =    0.0274

. eststo mA5Ab: reg pandemicsupport tech for elite techX
> jobeffectCovid3 forXjobeffectCovid3 eliteXjobeffectCov
> id3 jobeffectCovid3 if trumpsupporter==1, robust

Linear regression                               Number o
> f obs     =        958
                                                F(7, 950
> )         =       6.69
                                                Prob > F
>           =     0.0000
                                                R-square
> d         =     0.0515
                                                Root MSE
>           =     1.0459

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |   .1200144   .1548393     0.78   0.438   
>  -.1838521                                            
>                .423881
         for |   .0720621   .1525037     0.47   0.637   
>  -.2272209                                            
>                .371345
       elite |  -.0145878   .1539325    -0.09   0.925   
>  -.3166749                                            
>               .2874992
techXjobef~3 |   .1024891    .420818     0.24   0.808   
>  -.7233512                                            
>               .9283295
forXjobeff~3 |   .3321235   .4440485     0.75   0.455   
>  -.5393058                                            
>               1.203553
eliteXjobe~3 |   .2486012   .4326737     0.57   0.566   
>  -.6005056                                            
>               1.097708
jobeffectC~3 |   .4036525   .3980633     1.01   0.311   
>  -.3775325                                            
>               1.184837
       _cons |   3.017815   .1343959    22.45   0.000   
>   2.754068                                            
>               3.281562
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (elite + eliteXjo
> beffectCovid3)

 ( 1)  for - elite + forXjobeffectCovid3 -
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .1701722   .2163819     0.79   0.432   
>  -.2544695                                            
>               .5948139
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (tech + techXjobe
> ffectCovid3)

 ( 1)  - tech + for - techXjobeffectCovid3 +
       forXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |    .181682   .2013822     0.90   0.367   
>  -.2135234                                            
>               .5768874
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXjobeffectCovid3) - (tech + techX
> jobeffectCovid3)

 ( 1)  - tech + elite - techXjobeffectCovid3 +
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .0115098   .1842373     0.06   0.950   
>  -.3500493                                            
>                .373069
--------------------------------------------------------
> ----------------------

. test for + forXjobeffectCovid3=elite + eliteXjobeffect
> Covid3

 ( 1)  for - elite + forXjobeffectCovid3 -
       eliteXjobeffectCovid3 = 0

       F(  1,   950) =    0.62
            Prob > F =    0.4318

. test for + forXjobeffectCovid3 = tech + techXjobeffect
> Covid3

 ( 1)  - tech + for - techXjobeffectCovid3 +
       forXjobeffectCovid3 = 0

       F(  1,   950) =    0.81
            Prob > F =    0.3672

. test elite + eliteXjobeffectCovid3 = tech + techXjobef
> fectCovid3

 ( 1)  - tech + elite - techXjobeffectCovid3 +
       eliteXjobeffectCovid3 = 0

       F(  1,   950) =    0.00
            Prob > F =    0.9502

. esttab mA5Aa mA5Ab using "figures_tables/Appendixtable
> F6A.rtf", order(for elite tech knowCovid forXknow elit
> eXknow techXknow jobeffectCovid3 forXjobeffectCovid3 e
> liteXjobeffectCovid3 techXjobeffectCovid3) $esttabform
> at replace label onecell
(output written to figures_tables/AppendixtableF6A.rtf)

. *lincom estimates added manually
. 
. *********************************************
. *TABLE F6B: EFFECTS FOR NON-TRUMP SUPPORTERS*
. *********************************************
. 
. eststo mA5Ba: reg pandemicsupport tech for elite techX
> know forXknow eliteXknow knowCovid if trumpsupporter==
> 0, robust

Linear regression                               Number o
> f obs     =      1,037
                                                F(7, 102
> 9)        =       1.05
                                                Prob > F
>           =     0.3916
                                                R-square
> d         =     0.0086
                                                Root MSE
>           =     .94832

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |   .0122888    .117521     0.10   0.917   
>  -.2183195                                            
>               .2428971
         for |  -.1152097   .1193891    -0.96   0.335   
>  -.3494837                                            
>               .1190642
       elite |  -.1085428   .1167961    -0.93   0.353   
>  -.3377286                                            
>                .120643
   techXknow |   .1933115   .3536406     0.55   0.585   
>  -.5006276                                            
>               .8872505
    forXknow |   .3143437   .3212902     0.98   0.328   
>   -.316115                                            
>               .9448025
  eliteXknow |   .1309497   .3383515     0.39   0.699   
>   -.532988                                            
>               .7948874
   knowCovid |   -.366179   .2737107    -1.34   0.181   
>  -.9032738                                            
>               .1709158
       _cons |   3.799871   .0936017    40.60   0.000   
>   3.616199                                            
>               3.983543
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (elite + eliteXknow)

 ( 1)  for - elite + forXknow - eliteXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .1767271    .210504     0.84   0.401   
>  -.2363391                                            
>               .5897933
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (tech + techXknow)

 ( 1)  - tech + for - techXknow + forXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.0064663    .228685    -0.03   0.977   
>  -.4552084                                            
>               .4422758
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXknow) - (tech + techXknow)

 ( 1)  - tech + elite - techXknow + eliteXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.1831934   .2509239    -0.73   0.466   
>  -.6755743                                            
>               .3091875
--------------------------------------------------------
> ----------------------

. test for + forXknow=elite + eliteXknow

 ( 1)  for - elite + forXknow - eliteXknow = 0

       F(  1,  1029) =    0.70
            Prob > F =    0.4014

. test for + forXknow = tech + techXknow

 ( 1)  - tech + for - techXknow + forXknow = 0

       F(  1,  1029) =    0.00
            Prob > F =    0.9774

. test elite + eliteXknow = tech + techXknow

 ( 1)  - tech + elite - techXknow + eliteXknow = 0

       F(  1,  1029) =    0.53
            Prob > F =    0.4655

. eststo mA5Bb: reg pandemicsupport tech for elite techX
> jobeffectCovid3 forXjobeffectCovid3 eliteXjobeffectCov
> id3 jobeffectCovid3 if trumpsupporter==0, robust

Linear regression                               Number o
> f obs     =      1,037
                                                F(7, 102
> 9)        =       1.28
                                                Prob > F
>           =     0.2561
                                                R-square
> d         =     0.0111
                                                Root MSE
>           =     .94715

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |  -.0036898   .1085274    -0.03   0.973   
>    -.21665                                            
>               .2092705
         for |  -.1358635   .1079815    -1.26   0.209   
>  -.3477525                                            
>               .0760256
       elite |  -.0556907   .1085294    -0.51   0.608   
>   -.268655                                            
>               .1572736
techXjobef~3 |   .2943734   .3196089     0.92   0.357   
>  -.3327862                                            
>               .9215329
forXjobeff~3 |   .4459537   .2729602     1.63   0.103   
>  -.0896685                                            
>               .9815759
eliteXjobe~3 |  -.1036617   .2994355    -0.35   0.729   
>  -.6912356                                            
>               .4839123
jobeffectC~3 |  -.2872739   .2136982    -1.34   0.179   
>  -.7066079                                            
>                 .13206
       _cons |   3.773185   .0861526    43.80   0.000   
>    3.60413                                            
>                3.94224
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (elite + eliteXjo
> beffectCovid3)

 ( 1)  for - elite + forXjobeffectCovid3 -
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .4694426   .2322356     2.02   0.043   
>   .0137331                                            
>                .925152
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (tech + techXjobe
> ffectCovid3)

 ( 1)  - tech + for - techXjobeffectCovid3 +
       forXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .0194066   .2540762     0.08   0.939   
>  -.4791601                                            
>               .5179733
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXjobeffectCovid3) - (tech + techX
> jobeffectCovid3)

 ( 1)  - tech + elite - techXjobeffectCovid3 +
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   -.450036   .2766076    -1.63   0.104   
>  -.9928154                                            
>               .0927435
--------------------------------------------------------
> ----------------------

. test for + forXjobeffectCovid3=elite + eliteXjobeffect
> Covid3

 ( 1)  for - elite + forXjobeffectCovid3 -
       eliteXjobeffectCovid3 = 0

       F(  1,  1029) =    4.09
            Prob > F =    0.0435

. test for + forXjobeffectCovid3 = tech + techXjobeffect
> Covid3

 ( 1)  - tech + for - techXjobeffectCovid3 +
       forXjobeffectCovid3 = 0

       F(  1,  1029) =    0.01
            Prob > F =    0.9391

. test elite + eliteXjobeffectCovid3 = tech + techXjobef
> fectCovid3

 ( 1)  - tech + elite - techXjobeffectCovid3 +
       eliteXjobeffectCovid3 = 0

       F(  1,  1029) =    2.65
            Prob > F =    0.1040

. esttab mA5Ba mA5Bb using "figures_tables/Appendixtable
> F6B.rtf", order(for elite tech knowCovid forXknow elit
> eXknow techXknow jobeffectCovid3 forXjobeffectCovid3 e
> liteXjobeffectCovid3 techXjobeffectCovid3) $esttabform
> at replace label onecell
(output written to figures_tables/AppendixtableF6B.rtf)

. *lincom estimates added manually
. 
. ************************************************
. *TABLE F7A: EFFECTS FOR HIGH EDUCATION SUBGROUP*
. ************************************************
. 
. eststo mA6Aa: reg pandemicsupport tech for elite techX
> know forXknow eliteXknow knowCovid if CollegeDegree==1
> , robust

Linear regression                               Number o
> f obs     =        919
                                                F(7, 911
> )         =       2.68
                                                Prob > F
>           =     0.0094
                                                R-square
> d         =     0.0213
                                                Root MSE
>           =     1.0617

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |   -.045382   .1504226    -0.30   0.763   
>   -.340597                                            
>                .249833
         for |  -.2544847   .1494169    -1.70   0.089   
>  -.5477261                                            
>               .0387566
       elite |  -.1917723   .1525556    -1.26   0.209   
>  -.4911736                                            
>                .107629
   techXknow |   .5506709   .3371092     1.63   0.103   
>    -.11093                                            
>               1.212272
    forXknow |   .8438242   .3249331     2.60   0.010   
>   .2061197                                            
>               1.481529
  eliteXknow |   .4618125   .3370558     1.37   0.171   
>  -.1996836                                            
>               1.123309
   knowCovid |   -.303343    .283545    -1.07   0.285   
>  -.8598204                                            
>               .2531344
       _cons |    3.57792   .1229185    29.11   0.000   
>   3.336684                                            
>               3.819156
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (elite + eliteXknow)

 ( 1)  for - elite + forXknow - eliteXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .3192993   .1802066     1.77   0.077   
>   -.034369                                            
>               .6729676
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (tech + techXknow)

 ( 1)  - tech + for - techXknow + forXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .0840505   .1841419     0.46   0.648   
>  -.2773412                                            
>               .4454423
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXknow) - (tech + techXknow)

 ( 1)  - tech + elite - techXknow + eliteXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.2352487   .1960883    -1.20   0.231   
>   -.620086                                            
>               .1495885
--------------------------------------------------------
> ----------------------

. test for + forXknow=elite + eliteXknow

 ( 1)  for - elite + forXknow - eliteXknow = 0

       F(  1,   911) =    3.14
            Prob > F =    0.0768

. test for + forXknow = tech + techXknow

 ( 1)  - tech + for - techXknow + forXknow = 0

       F(  1,   911) =    0.21
            Prob > F =    0.6482

. test elite + eliteXknow = tech + techXknow

 ( 1)  - tech + elite - techXknow + eliteXknow = 0

       F(  1,   911) =    1.44
            Prob > F =    0.2306

. eststo mA6Ab: reg pandemicsupport tech for elite techX
> jobeffectCovid3 forXjobeffectCovid3 eliteXjobeffectCov
> id3 jobeffectCovid3 if CollegeDegree==1, robust

Linear regression                               Number o
> f obs     =        919
                                                F(7, 911
> )         =       2.36
                                                Prob > F
>           =     0.0215
                                                R-square
> d         =     0.0174
                                                Root MSE
>           =     1.0638

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |   .1355924   .1447457     0.94   0.349   
>  -.1484814                                            
>               .4196662
         for |  -.0713632   .1432805    -0.50   0.619   
>  -.3525615                                            
>               .2098351
       elite |  -.0424902   .1441421    -0.29   0.768   
>  -.3253794                                            
>               .2403989
techXjobef~3 |   .0153887   .3109368     0.05   0.961   
>   -.594847                                            
>               .6256244
forXjobeff~3 |   .4239208   .3108704     1.36   0.173   
>  -.1861846                                            
>               1.034026
eliteXjobe~3 |   .0514564   .3132048     0.16   0.870   
>  -.5632303                                            
>               .6661432
jobeffectC~3 |   .1253838   .2620936     0.48   0.632   
>  -.3889936                                            
>               .6397612
       _cons |   3.438833   .1215892    28.28   0.000   
>   3.200206                                            
>               3.677461
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (elite + eliteXjo
> beffectCovid3)

 ( 1)  for - elite + forXjobeffectCovid3 -
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .3435914   .2000123     1.72   0.086   
>  -.0489469                                            
>               .7361298
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (tech + techXjobe
> ffectCovid3)

 ( 1)  - tech + for - techXjobeffectCovid3 +
       forXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .2015765   .1960351     1.03   0.304   
>  -.1831563                                            
>               .5863093
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXjobeffectCovid3) - (tech + techX
> jobeffectCovid3)

 ( 1)  - tech + elite - techXjobeffectCovid3 +
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.1420149    .201096    -0.71   0.480   
>  -.5366802                                            
>               .2526504
--------------------------------------------------------
> ----------------------

. test for + forXjobeffectCovid3=elite + eliteXjobeffect
> Covid3

 ( 1)  for - elite + forXjobeffectCovid3 -
       eliteXjobeffectCovid3 = 0

       F(  1,   911) =    2.95
            Prob > F =    0.0862

. test for + forXjobeffectCovid3 = tech + techXjobeffect
> Covid3

 ( 1)  - tech + for - techXjobeffectCovid3 +
       forXjobeffectCovid3 = 0

       F(  1,   911) =    1.06
            Prob > F =    0.3041

. test elite + eliteXjobeffectCovid3 = tech + techXjobef
> fectCovid3

 ( 1)  - tech + elite - techXjobeffectCovid3 +
       eliteXjobeffectCovid3 = 0

       F(  1,   911) =    0.50
            Prob > F =    0.4802

. esttab mA6Aa mA6Ab using "figures_tables/Appendixtable
> F7A.rtf", order(for elite tech knowCovid forXknow elit
> eXknow techXknow jobeffectCovid3 forXjobeffectCovid3 e
> liteXjobeffectCovid3 techXjobeffectCovid3) $esttabform
> at replace label onecell
(output written to figures_tables/AppendixtableF7A.rtf)

. 
. ***********************************************
. *TABLE F7B: EFFECTS FOR LOW EDUCATION SUBGROUP*
. ***********************************************
. 
. eststo mA6Ba: reg pandemicsupport tech for elite techX
> know forXknow eliteXknow knowCovid if CollegeDegree==0
> , robust

Linear regression                               Number o
> f obs     =        766
                                                F(7, 758
> )         =       1.15
                                                Prob > F
>           =     0.3311
                                                R-square
> d         =     0.0112
                                                Root MSE
>           =     1.0091

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |  -.0600335   .1567742    -0.38   0.702   
>  -.3677966                                            
>               .2477297
         for |  -.2154446   .1464013    -1.47   0.142   
>  -.5028447                                            
>               .0719554
       elite |  -.0100889   .1482637    -0.07   0.946   
>   -.301145                                            
>               .2809673
   techXknow |   .4688453    .394785     1.19   0.235   
>  -.3061566                                            
>               1.243847
    forXknow |   .6228553   .3503251     1.78   0.076   
>  -.0648675                                            
>               1.310578
  eliteXknow |   .0373045   .3861118     0.10   0.923   
>  -.7206711                                            
>               .7952801
   knowCovid |  -.4786072   .2837333    -1.69   0.092   
>  -1.035604                                            
>               .0783893
       _cons |   3.489885   .1236034    28.23   0.000   
>    3.24724                                            
>               3.732531
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (elite + eliteXknow)

 ( 1)  for - elite + forXknow - eliteXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |    .380195   .2839242     1.34   0.181   
>  -.1771761                                            
>               .9375661
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (tech + techXknow)

 ( 1)  - tech + for - techXknow + forXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.0014012   .2839823    -0.00   0.996   
>  -.5588864                                            
>               .5560841
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXknow) - (tech + techXknow)

 ( 1)  - tech + elite - techXknow + eliteXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.3815962   .3202672    -1.19   0.234   
>  -1.010312                                            
>               .2471199
--------------------------------------------------------
> ----------------------

. test for + forXknow=elite + eliteXknow

 ( 1)  for - elite + forXknow - eliteXknow = 0

       F(  1,   758) =    1.79
            Prob > F =    0.1809

. test for + forXknow = tech + techXknow

 ( 1)  - tech + for - techXknow + forXknow = 0

       F(  1,   758) =    0.00
            Prob > F =    0.9961

. test elite + eliteXknow = tech + techXknow

 ( 1)  - tech + elite - techXknow + eliteXknow = 0

       F(  1,   758) =    1.42
            Prob > F =    0.2338

. eststo mA6Bb: reg pandemicsupport tech for elite techX
> jobeffectCovid3 forXjobeffectCovid3 eliteXjobeffectCov
> id3 jobeffectCovid3 if CollegeDegree==0, robust

Linear regression                               Number o
> f obs     =        766
                                                F(7, 758
> )         =       0.31
                                                Prob > F
>           =     0.9492
                                                R-square
> d         =     0.0031
                                                Root MSE
>           =     1.0132

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |   .0311673    .146363     0.21   0.831   
>  -.2561577                                            
>               .3184922
         for |  -.1034835   .1396815    -0.74   0.459   
>  -.3776921                                            
>                .170725
       elite |   .0144757   .1477094     0.10   0.922   
>  -.2754925                                            
>               .3044439
techXjobef~3 |   .1643715   .4736591     0.35   0.729   
>  -.7654679                                            
>               1.094211
forXjobeff~3 |   .2217141   .4119993     0.54   0.591   
>  -.5870811                                            
>               1.030509
eliteXjobe~3 |   .0554539    .474244     0.12   0.907   
>  -.8755339                                            
>               .9864416
jobeffectC~3 |  -.1027496   .3579522    -0.29   0.774   
>   -.805445                                            
>               .5999458
       _cons |   3.385133   .1176604    28.77   0.000   
>   3.154154                                            
>               3.616112
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (elite + eliteXjo
> beffectCovid3)

 ( 1)  for - elite + forXjobeffectCovid3 -
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .0483009   .3159232     0.15   0.879   
>  -.5718874                                            
>               .6684892
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (tech + techXjobe
> ffectCovid3)

 ( 1)  - tech + for - techXjobeffectCovid3 +
       forXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.0773082   .3258077    -0.24   0.813   
>  -.7169009                                            
>               .5622844
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXjobeffectCovid3) - (tech + techX
> jobeffectCovid3)

 ( 1)  - tech + elite - techXjobeffectCovid3 +
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.1256092   .3800052    -0.33   0.741   
>  -.8715968                                            
>               .6203785
--------------------------------------------------------
> ----------------------

. test for + forXjobeffectCovid3=elite + eliteXjobeffect
> Covid3

 ( 1)  for - elite + forXjobeffectCovid3 -
       eliteXjobeffectCovid3 = 0

       F(  1,   758) =    0.02
            Prob > F =    0.8785

. test for + forXjobeffectCovid3 = tech + techXjobeffect
> Covid3

 ( 1)  - tech + for - techXjobeffectCovid3 +
       forXjobeffectCovid3 = 0

       F(  1,   758) =    0.06
            Prob > F =    0.8125

. test elite + eliteXjobeffectCovid3 = tech + techXjobef
> fectCovid3

 ( 1)  - tech + elite - techXjobeffectCovid3 +
       eliteXjobeffectCovid3 = 0

       F(  1,   758) =    0.11
            Prob > F =    0.7411

. esttab mA6Ba mA6Bb using "figures_tables/Appendixtable
> F7B.rtf", order(for elite tech knowCovid forXknow elit
> eXknow techXknow jobeffectCovid3 forXjobeffectCovid3 e
> liteXjobeffectCovid3 techXjobeffectCovid3) $esttabform
> at replace label onecell
(output written to figures_tables/AppendixtableF7B.rtf)

. *lincom estimates added manually*
. 
. *********************************************
. *TABLE F8A: EFFECTS FOR HIGH INCOME SUBGROUP*
. *********************************************
. 
. eststo mA7Aa: reg pandemicsupport tech for elite techX
> know forXknow eliteXknow knowCovid if hhi>12, robust

Linear regression                               Number o
> f obs     =      1,056
                                                F(7, 104
> 8)        =       2.72
                                                Prob > F
>           =     0.0084
                                                R-square
> d         =     0.0196
                                                Root MSE
>           =     1.0636

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |  -.1508091    .141911    -1.06   0.288   
>  -.4292712                                            
>                .127653
         for |   -.348584   .1425239    -2.45   0.015   
>  -.6282486                                            
>              -.0689193
       elite |  -.2596429   .1436745    -1.81   0.071   
>  -.5415654                                            
>               .0222796
   techXknow |   .7172526   .3143998     2.28   0.023   
>   .1003278                                            
>               1.334177
    forXknow |   .9446174    .311044     3.04   0.002   
>   .3342775                                            
>               1.554957
  eliteXknow |   .5986689   .3177438     1.88   0.060   
>  -.0248176                                            
>               1.222155
   knowCovid |  -.4388324   .2641995    -1.66   0.097   
>  -.9572526                                            
>               .0795877
       _cons |   3.642625    .117205    31.08   0.000   
>   3.412641                                            
>               3.872608
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (elite + eliteXknow)

 ( 1)  for - elite + forXknow - eliteXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .2570075   .1843146     1.39   0.163   
>  -.1046603                                            
>               .6186752
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (tech + techXknow)

 ( 1)  - tech + for - techXknow + forXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .0295899    .181825     0.16   0.871   
>  -.3271926                                            
>               .3863724
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXknow) - (tech + techXknow)

 ( 1)  - tech + elite - techXknow + eliteXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.2274176   .1887335    -1.20   0.228   
>  -.5977562                                            
>                .142921
--------------------------------------------------------
> ----------------------

. test for + forXknow=elite + eliteXknow

 ( 1)  for - elite + forXknow - eliteXknow = 0

       F(  1,  1048) =    1.94
            Prob > F =    0.1635

. test for + forXknow = tech + techXknow

 ( 1)  - tech + for - techXknow + forXknow = 0

       F(  1,  1048) =    0.03
            Prob > F =    0.8708

. test elite + eliteXknow = tech + techXknow

 ( 1)  - tech + elite - techXknow + eliteXknow = 0

       F(  1,  1048) =    1.45
            Prob > F =    0.2285

. eststo mA7Ab: reg pandemicsupport tech for elite techX
> jobeffectCovid3 forXjobeffectCovid3 eliteXjobeffectCov
> id3 jobeffectCovid3 if hhi>12, robust

Linear regression                               Number o
> f obs     =      1,056
                                                F(7, 104
> 8)        =       2.43
                                                Prob > F
>           =     0.0182
                                                R-square
> d         =     0.0176
                                                Root MSE
>           =     1.0647

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |   .1167475    .133091     0.88   0.381   
>  -.1444077                                            
>               .3779027
         for |  -.0235144   .1359336    -0.17   0.863   
>  -.2902474                                            
>               .2432185
       elite |   .0004396   .1399884     0.00   0.997   
>  -.2742498                                            
>               .2751291
techXjobef~3 |  -.0876462   .2612049    -0.34   0.737   
>  -.6001902                                            
>               .4248979
forXjobeff~3 |   .0535691   .2839628     0.19   0.850   
>  -.5036313                                            
>               .6107695
eliteXjobe~3 |  -.1712359   .3095006    -0.55   0.580   
>  -.7785472                                            
>               .4360754
jobeffectC~3 |   .3895295   .2143584     1.82   0.069   
>   -.031091                                            
>               .8101499
       _cons |   3.375976   .1128239    29.92   0.000   
>    3.15459                                            
>               3.597363
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (elite + eliteXjo
> beffectCovid3)

 ( 1)  for - elite + forXjobeffectCovid3 -
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .2008509    .234527     0.86   0.392   
>   -.259345                                            
>               .6610469
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (tech + techXjobe
> ffectCovid3)

 ( 1)  - tech + for - techXjobeffectCovid3 +
       forXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .0009534   .1981904     0.00   0.996   
>  -.3879417                                            
>               .3898485
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXjobeffectCovid3) - (tech + techX
> jobeffectCovid3)

 ( 1)  - tech + elite - techXjobeffectCovid3 +
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.1998976   .2179832    -0.92   0.359   
>  -.6276308                                            
>               .2278356
--------------------------------------------------------
> ----------------------

. test for + forXjobeffectCovid3=elite + eliteXjobeffect
> Covid3

 ( 1)  for - elite + forXjobeffectCovid3 -
       eliteXjobeffectCovid3 = 0

       F(  1,  1048) =    0.73
            Prob > F =    0.3920

. test for + forXjobeffectCovid3 = tech + techXjobeffect
> Covid3

 ( 1)  - tech + for - techXjobeffectCovid3 +
       forXjobeffectCovid3 = 0

       F(  1,  1048) =    0.00
            Prob > F =    0.9962

. test elite + eliteXjobeffectCovid3 = tech + techXjobef
> fectCovid3

 ( 1)  - tech + elite - techXjobeffectCovid3 +
       eliteXjobeffectCovid3 = 0

       F(  1,  1048) =    0.84
            Prob > F =    0.3593

. esttab mA7Aa mA7Ab using "figures_tables/Appendixtable
> F8A.rtf", order(for elite tech knowCovid forXknow elit
> eXknow techXknow jobeffectCovid3 forXjobeffectCovid3 e
> liteXjobeffectCovid3 techXjobeffectCovid3) $esttabform
> at replace label onecell
(output written to figures_tables/AppendixtableF8A.rtf)

. 
. ********************************************
. *TABLE F8B: EFFECTS FOR LOW INCOME SUBGROUP*
. ********************************************
. 
. eststo mA7Ba: reg pandemicsupport tech for elite techX
> know forXknow eliteXknow knowCovid if hhi<13, robust

Linear regression                               Number o
> f obs     =        939
                                                F(7, 931
> )         =       1.86
                                                Prob > F
>           =     0.0723
                                                R-square
> d         =     0.0144
                                                Root MSE
>           =     .98563

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |   -.001679   .1387581    -0.01   0.990   
>  -.2739939                                            
>                .270636
         for |  -.1153235   .1292904    -0.89   0.373   
>  -.3690579                                            
>               .1384109
       elite |   .0058529    .131197     0.04   0.964   
>  -.2516231                                            
>                .263329
   techXknow |   .3591857   .3686354     0.97   0.330   
>  -.3642669                                            
>               1.082638
    forXknow |   .7039141    .331449     2.12   0.034   
>   .0534403                                            
>               1.354388
  eliteXknow |  -.1119836   .3546884    -0.32   0.752   
>  -.8080649                                            
>               .5840978
   knowCovid |  -.2657935   .2883092    -0.92   0.357   
>  -.8316048                                            
>               .3000178
       _cons |   3.486004   .1086381    32.09   0.000   
>     3.2728                                            
>               3.699208
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (elite + eliteXknow)

 ( 1)  for - elite + forXknow - eliteXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .6947212   .2195034     3.16   0.002   
>   .2639424                                            
>                 1.1255
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (tech + techXknow)

 ( 1)  - tech + for - techXknow + forXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .2310839   .2290623     1.01   0.313   
>  -.2184543                                            
>               .6806221
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXknow) - (tech + techXknow)

 ( 1)  - tech + elite - techXknow + eliteXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.4636373   .2560195    -1.81   0.070   
>  -.9660794                                            
>               .0388048
--------------------------------------------------------
> ----------------------

. test for + forXknow=elite + eliteXknow

 ( 1)  for - elite + forXknow - eliteXknow = 0

       F(  1,   931) =   10.02
            Prob > F =    0.0016

. test for + forXknow = tech + techXknow

 ( 1)  - tech + for - techXknow + forXknow = 0

       F(  1,   931) =    1.02
            Prob > F =    0.3133

. test elite + eliteXknow = tech + techXknow

 ( 1)  - tech + elite - techXknow + eliteXknow = 0

       F(  1,   931) =    3.28
            Prob > F =    0.0705

. eststo mA7Bb: reg pandemicsupport tech for elite techX
> jobeffectCovid3 forXjobeffectCovid3 eliteXjobeffectCov
> id3 jobeffectCovid3 if hhi<13, robust

Linear regression                               Number o
> f obs     =        939
                                                F(7, 931
> )         =       1.58
                                                Prob > F
>           =     0.1372
                                                R-square
> d         =     0.0108
                                                Root MSE
>           =     .98745

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |    .041983   .1216599     0.35   0.730   
>  -.1967763                                            
>               .2807424
         for |  -.1039634   .1153544    -0.90   0.368   
>  -.3303481                                            
>               .1224213
       elite |  -.0635665   .1211066    -0.52   0.600   
>  -.3012402                                            
>               .1741071
techXjobef~3 |    .196948   .3445059     0.57   0.568   
>  -.4791501                                            
>               .8730461
forXjobeff~3 |   .7420885   .2814559     2.64   0.009   
>   .1897271                                            
>                1.29445
eliteXjobe~3 |   .2124779   .3217971     0.66   0.509   
>  -.4190538                                            
>               .8440097
jobeffectC~3 |  -.2995647   .2289624    -1.31   0.191   
>   -.748907                                            
>               .1497775
       _cons |   3.484901   .0945851    36.84   0.000   
>   3.299276                                            
>               3.670526
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (elite + eliteXjo
> beffectCovid3)

 ( 1)  for - elite + forXjobeffectCovid3 -
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .4892138    .236642     2.07   0.039   
>   .0248001                                            
>               .9536274
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (tech + techXjobe
> ffectCovid3)

 ( 1)  - tech + for - techXjobeffectCovid3 +
       forXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .3991942   .2667761     1.50   0.135   
>   -.124358                                            
>               .9227464
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXjobeffectCovid3) - (tech + techX
> jobeffectCovid3)

 ( 1)  - tech + elite - techXjobeffectCovid3 +
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.0900196   .2970733    -0.30   0.762   
>  -.6730305                                            
>               .4929914
--------------------------------------------------------
> ----------------------

. test for + forXjobeffectCovid3=elite + eliteXjobeffect
> Covid3

 ( 1)  for - elite + forXjobeffectCovid3 -
       eliteXjobeffectCovid3 = 0

       F(  1,   931) =    4.27
            Prob > F =    0.0390

. test for + forXjobeffectCovid3 = tech + techXjobeffect
> Covid3

 ( 1)  - tech + for - techXjobeffectCovid3 +
       forXjobeffectCovid3 = 0

       F(  1,   931) =    2.24
            Prob > F =    0.1349

. test elite + eliteXjobeffectCovid3 = tech + techXjobef
> fectCovid3

 ( 1)  - tech + elite - techXjobeffectCovid3 +
       eliteXjobeffectCovid3 = 0

       F(  1,   931) =    0.09
            Prob > F =    0.7619

. esttab mA7Ba mA7Bb using "figures_tables/Appendixtable
> F8B.rtf", order(for elite tech knowCovid forXknow elit
> eXknow techXknow jobeffectCovid3 forXjobeffectCovid3 e
> liteXjobeffectCovid3 techXjobeffectCovid3) $esttabform
> at replace label onecell
(output written to figures_tables/AppendixtableF8B.rtf)

. *lincom estimates added manually
. 
. ******************************************************
> *****
. *TABLE F9A: EFFECTS FOR HIGH TRUST IN REPUBLICANS SUBG
> ROUP*
. ******************************************************
> *****
. 
. eststo mA8Aa: reg pandemicsupport tech for elite techX
> know forXknow eliteXknow knowCovid if Republicanbetter
> ==1, robust

Linear regression                               Number o
> f obs     =        666
                                                F(7, 658
> )         =       8.52
                                                Prob > F
>           =     0.0000
                                                R-square
> d         =     0.0831
                                                Root MSE
>           =     1.0916

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |   .0455371   .2076381     0.22   0.826   
>   -.362176                                            
>               .4532503
         for |  -.0727993   .1954276    -0.37   0.710   
>  -.4565362                                            
>               .3109376
       elite |   .1716098   .1973741     0.87   0.385   
>  -.2159492                                            
>               .5591688
   techXknow |   1.095393   .4397508     2.49   0.013   
>   .2319086                                            
>               1.958877
    forXknow |   1.268965   .4241204     2.99   0.003   
>   .4361724                                            
>               2.101758
  eliteXknow |   .5042193   .4430871     1.14   0.256   
>  -.3658157                                            
>               1.374254
   knowCovid |  -.2862483   .3881912    -0.74   0.461   
>  -1.048491                                            
>               .4759946
       _cons |   3.090564   .1705228    18.12   0.000   
>    2.75573                                            
>               3.425398
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (elite + eliteXknow)

 ( 1)  for - elite + forXknow - eliteXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .5203366    .210894     2.47   0.014   
>   .1062302                                            
>                .934443
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (tech + techXknow)

 ( 1)  - tech + for - techXknow + forXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .0552359   .1949498     0.28   0.777   
>  -.3275629                                            
>               .4380347
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXknow) - (tech + techXknow)

 ( 1)  - tech + elite - techXknow + eliteXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.4651007   .2208323    -2.11   0.036   
>  -.8987217                                            
>              -.0314797
--------------------------------------------------------
> ----------------------

. test for + forXknow=elite + eliteXknow

 ( 1)  for - elite + forXknow - eliteXknow = 0

       F(  1,   658) =    6.09
            Prob > F =    0.0139

. test for + forXknow = tech + techXknow

 ( 1)  - tech + for - techXknow + forXknow = 0

       F(  1,   658) =    0.08
            Prob > F =    0.7770

. test elite + eliteXknow = tech + techXknow

 ( 1)  - tech + elite - techXknow + eliteXknow = 0

       F(  1,   658) =    4.44
            Prob > F =    0.0356

. eststo mA8Ab: reg pandemicsupport tech for elite techX
> jobeffectCovid3 forXjobeffectCovid3 eliteXjobeffectCov
> id3 jobeffectCovid3 if Republicanbetter==1, robust

Linear regression                               Number o
> f obs     =        666
                                                F(7, 658
> )         =       6.75
                                                Prob > F
>           =     0.0000
                                                R-square
> d         =     0.0608
                                                Root MSE
>           =     1.1048

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |   .2857697   .1840691     1.55   0.121   
>   -.075664                                            
>               .6472034
         for |   .1913115   .1818663     1.05   0.293   
>  -.1657968                                            
>               .5484197
       elite |   .3103454   .1898965     1.63   0.103   
>  -.0625307                                            
>               .6832215
techXjobef~3 |    .476279   .4156704     1.15   0.252   
>  -.3399213                                            
>               1.292479
forXjobeff~3 |   .6929371   .4333811     1.60   0.110   
>  -.1580394                                            
>               1.543914
eliteXjobe~3 |   .1322305   .4653064     0.28   0.776   
>  -.7814338                                            
>               1.045895
jobeffectC~3 |    .110528   .3912731     0.28   0.778   
>  -.6577664                                            
>               .8788225
       _cons |   2.948442   .1560636    18.89   0.000   
>   2.641999                                            
>               3.254885
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (elite + eliteXjo
> beffectCovid3)

 ( 1)  for - elite + forXjobeffectCovid3 -
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .4416727   .2418077     1.83   0.068   
>   -.033135                                            
>               .9164804
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (tech + techXjobe
> ffectCovid3)

 ( 1)  - tech + for - techXjobeffectCovid3 +
       forXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .1221999    .192165     0.64   0.525   
>  -.2551307                                            
>               .4995305
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXjobeffectCovid3) - (tech + techX
> jobeffectCovid3)

 ( 1)  - tech + elite - techXjobeffectCovid3 +
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.3194728   .2254098    -1.42   0.157   
>   -.762082                                            
>               .1231364
--------------------------------------------------------
> ----------------------

. test for + forXjobeffectCovid3=elite + eliteXjobeffect
> Covid3

 ( 1)  for - elite + forXjobeffectCovid3 -
       eliteXjobeffectCovid3 = 0

       F(  1,   658) =    3.34
            Prob > F =    0.0682

. test for + forXjobeffectCovid3 = tech + techXjobeffect
> Covid3

 ( 1)  - tech + for - techXjobeffectCovid3 +
       forXjobeffectCovid3 = 0

       F(  1,   658) =    0.40
            Prob > F =    0.5251

. test elite + eliteXjobeffectCovid3 = tech + techXjobef
> fectCovid3

 ( 1)  - tech + elite - techXjobeffectCovid3 +
       eliteXjobeffectCovid3 = 0

       F(  1,   658) =    2.01
            Prob > F =    0.1569

. esttab mA8Aa mA8Ab using "figures_tables/Appendixtable
> F9A.rtf", order(for elite tech knowCovid forXknow elit
> eXknow techXknow jobeffectCovid3 forXjobeffectCovid3 e
> liteXjobeffectCovid3 techXjobeffectCovid3) $esttabform
> at replace label onecell
(output written to figures_tables/AppendixtableF9A.rtf)

. *add linear combinations manually
. 
. ******************************************************
> ***
. *TABLE F9B: EFFECTS FOR HIGH TRUST IN DEMOCRATS SUBGRO
> UP*
. ******************************************************
> ***
. 
. eststo mA8Ba: reg pandemicsupport tech for elite techX
> know forXknow eliteXknow knowCovid if Democratbetter==
> 1, robust

Linear regression                               Number o
> f obs     =        721
                                                F(7, 713
> )         =       2.29
                                                Prob > F
>           =     0.0257
                                                R-square
> d         =     0.0327
                                                Root MSE
>           =     1.0186

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |  -.0779254   .1436035    -0.54   0.588   
>  -.3598616                                            
>               .2040108
         for |  -.1203174   .1407008    -0.86   0.393   
>  -.3965548                                            
>               .1559201
       elite |  -.0020257   .1412049    -0.01   0.989   
>  -.2792528                                            
>               .2752015
   techXknow |   .6013905   .3428449     1.75   0.080   
>  -.0717158                                            
>               1.274497
    forXknow |   .8567894   .3210678     2.67   0.008   
>    .226438                                            
>               1.487141
  eliteXknow |   .2500552   .3411378     0.73   0.464   
>  -.4196994                                            
>               .9198099
   knowCovid |  -.7643047   .2748035    -2.78   0.006   
>  -1.303826                                            
>              -.2247839
       _cons |   4.045215   .1068693    37.85   0.000   
>   3.835399                                            
>               4.255031
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (elite + eliteXknow)

 ( 1)  for - elite + forXknow - eliteXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .4884424    .202444     2.41   0.016   
>   .0909847                                            
>               .8859001
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (tech + techXknow)

 ( 1)  - tech + for - techXknow + forXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .2130069   .1999943     1.07   0.287   
>  -.1796413                                            
>               .6056551
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXknow) - (tech + techXknow)

 ( 1)  - tech + elite - techXknow + eliteXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.2754356   .2250977    -1.22   0.221   
>   -.717369                                            
>               .1664979
--------------------------------------------------------
> ----------------------

. test for + forXknow=elite + eliteXknow

 ( 1)  for - elite + forXknow - eliteXknow = 0

       F(  1,   713) =    5.82
            Prob > F =    0.0161

. test for + forXknow = tech + techXknow

 ( 1)  - tech + for - techXknow + forXknow = 0

       F(  1,   713) =    1.13
            Prob > F =    0.2872

. test elite + eliteXknow = tech + techXknow

 ( 1)  - tech + elite - techXknow + eliteXknow = 0

       F(  1,   713) =    1.50
            Prob > F =    0.2215

. eststo mA8Bb: reg pandemicsupport tech for elite techX
> jobeffectCovid3 forXjobeffectCovid3 eliteXjobeffectCov
> id3 jobeffectCovid3 if Democratbetter==1, robust

Linear regression                               Number o
> f obs     =        721
                                                F(7, 713
> )         =       2.54
                                                Prob > F
>           =     0.0139
                                                R-square
> d         =     0.0240
                                                Root MSE
>           =     1.0232

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |  -.0466843   .1352522    -0.35   0.730   
>  -.3122244                                            
>               .2188559
         for |  -.0128448   .1355807    -0.09   0.925   
>    -.27903                                            
>               .2533403
       elite |  -.0138344   .1432667    -0.10   0.923   
>  -.2951094                                            
>               .2674405
techXjobef~3 |   .6062682   .2425677     2.50   0.013   
>   .1300359                                            
>                 1.0825
forXjobeff~3 |   .6737583   .2388749     2.82   0.005   
>   .2047759                                            
>               1.142741
eliteXjobe~3 |   .2973703   .2742327     1.08   0.279   
>  -.2410298                                            
>               .8357704
jobeffectC~3 |  -.6203829   .1751566    -3.54   0.000   
>  -.9642672                                            
>              -.2764986
       _cons |   3.957506   .1073029    36.88   0.000   
>   3.746838                                            
>               4.168173
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (elite + eliteXjo
> beffectCovid3)

 ( 1)  for - elite + forXjobeffectCovid3 -
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .3773776   .2150677     1.75   0.080   
>  -.0448641                                            
>               .7996192
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (tech + techXjobe
> ffectCovid3)

 ( 1)  - tech + for - techXjobeffectCovid3 +
       forXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .1013295   .1952971     0.52   0.604   
>  -.2820967                                            
>               .4847557
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXjobeffectCovid3) - (tech + techX
> jobeffectCovid3)

 ( 1)  - tech + elite - techXjobeffectCovid3 +
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.2760481    .223474    -1.24   0.217   
>  -.7147939                                            
>               .1626978
--------------------------------------------------------
> ----------------------

. test for + forXjobeffectCovid3=elite + eliteXjobeffect
> Covid3

 ( 1)  for - elite + forXjobeffectCovid3 -
       eliteXjobeffectCovid3 = 0

       F(  1,   713) =    3.08
            Prob > F =    0.0797

. test for + forXjobeffectCovid3 = tech + techXjobeffect
> Covid3

 ( 1)  - tech + for - techXjobeffectCovid3 +
       forXjobeffectCovid3 = 0

       F(  1,   713) =    0.27
            Prob > F =    0.6040

. test elite + eliteXjobeffectCovid3 = tech + techXjobef
> fectCovid3

 ( 1)  - tech + elite - techXjobeffectCovid3 +
       eliteXjobeffectCovid3 = 0

       F(  1,   713) =    1.53
            Prob > F =    0.2171

. esttab mA8Ba mA8Bb using "figures_tables/Appendixtable
> F9B.rtf", order(for elite tech knowCovid forXknow elit
> eXknow techXknow jobeffectCovid3 forXjobeffectCovid3 e
> liteXjobeffectCovid3 techXjobeffectCovid3) $esttabform
> at replace label onecell
(output written to figures_tables/AppendixtableF9B.rtf)

. *add linear combinations manually
. 
. ************
. *APPENDIX G*
. ************
. 
. *********
. *TABLE G1*
. *********
. 
. eststo m2a: reg econsupport tech for elite techXknow f
> orXknow eliteXknow knowCovid, robust

Linear regression                               Number o
> f obs     =      1,995
                                                F(7, 198
> 7)        =       6.68
                                                Prob > F
>           =     0.0000
                                                R-square
> d         =     0.0222
                                                Root MSE
>           =     1.2404

--------------------------------------------------------
> ----------------------
             |               Robust
 econsupport |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |  -.1296912   .1194796    -1.09   0.278   
>  -.3640096                                            
>               .1046272
         for |  -.2415733   .1154937    -2.09   0.037   
>  -.4680747                                            
>              -.0150719
       elite |   -.195967   .1168021    -1.68   0.094   
>  -.4250345                                            
>               .0331005
   techXknow |   .6425736   .2668927     2.41   0.016   
>   .1191548                                            
>               1.165992
    forXknow |   .9128661   .2524185     3.62   0.000   
>   .4178333                                            
>               1.407899
  eliteXknow |   .3682687   .2639821     1.40   0.163   
>   -.149442                                            
>               .8859795
   knowCovid |  -.1723591   .2144492    -0.80   0.422   
>  -.5929279                                            
>               .2482097
       _cons |    3.16757    .095717    33.09   0.000   
>   2.979853                                            
>               3.355286
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (elite + eliteXknow)

 ( 1)  for - elite + forXknow - eliteXknow = 0

--------------------------------------------------------
> ----------------------
 econsupport |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |    .498991   .1615135     3.09   0.002   
>   .1822374                                            
>               .8157446
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (tech + techXknow)

 ( 1)  - tech + for - techXknow + forXknow = 0

--------------------------------------------------------
> ----------------------
 econsupport |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .1584103   .1631121     0.97   0.332   
>  -.1614785                                            
>               .4782991
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXknow) - (tech + techXknow)

 ( 1)  - tech + elite - techXknow + eliteXknow = 0

--------------------------------------------------------
> ----------------------
 econsupport |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.3405807   .1758647    -1.94   0.053   
>  -.6854793                                            
>               .0043179
--------------------------------------------------------
> ----------------------

. eststo m2b: reg healthsupport tech for elite techXknow
>  forXknow eliteXknow knowCovid, robust

Linear regression                               Number o
> f obs     =      1,995
                                                F(7, 198
> 7)        =       2.59
                                                Prob > F
>           =     0.0116
                                                R-square
> d         =     0.0115
                                                Root MSE
>           =     1.0608

--------------------------------------------------------
> ----------------------
             |               Robust
healthsupp~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |  -.0320921   .0994196    -0.32   0.747   
>  -.2270696                                            
>               .1628855
         for |  -.2157889   .0987115    -2.19   0.029   
>  -.4093778                                            
>                 -.0222
       elite |  -.0607365   .0989025    -0.61   0.539   
>     -.2547                                            
>               .1332271
   techXknow |   .5243143   .2448617     2.14   0.032   
>   .0441018                                            
>               1.004527
    forXknow |   .7260886   .2389111     3.04   0.002   
>   .2575461                                            
>               1.194631
  eliteXknow |   .2454564   .2482997     0.99   0.323   
>  -.2414988                                            
>               .7324115
   knowCovid |  -.5403977   .2040399    -2.65   0.008   
>  -.9405523                                            
>              -.1402431
       _cons |   3.964821   .0804463    49.29   0.000   
>   3.807053                                            
>               4.122589
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (elite + eliteXknow)

 ( 1)  for - elite + forXknow - eliteXknow = 0

--------------------------------------------------------
> ----------------------
healthsupp~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .3255799   .1504798     2.16   0.031   
>   .0304652                                            
>               .6206946
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (tech + techXknow)

 ( 1)  - tech + for - techXknow + forXknow = 0

--------------------------------------------------------
> ----------------------
healthsupp~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .0180775    .144741     0.12   0.901   
>  -.2657825                                            
>               .3019375
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXknow) - (tech + techXknow)

 ( 1)  - tech + elite - techXknow + eliteXknow = 0

--------------------------------------------------------
> ----------------------
healthsupp~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.3075024   .1569952    -1.96   0.050   
>  -.6153948                                            
>                 .00039
--------------------------------------------------------
> ----------------------

. eststo m2c: reg broadensocsec tech for elite techXknow
>  forXknow eliteXknow knowCovid, robust

Linear regression                               Number o
> f obs     =      1,992
                                                F(7, 198
> 4)        =      14.70
                                                Prob > F
>           =     0.0000
                                                R-square
> d         =     0.0499
                                                Root MSE
>           =     1.3272

--------------------------------------------------------
> ----------------------
             |               Robust
broadensoc~c |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |  -.2327666   .1255879    -1.85   0.064   
>  -.4790646                                            
>               .0135315
         for |  -.3304202    .122988    -2.69   0.007   
>  -.5716194                                            
>               -.089221
       elite |  -.1697327   .1249356    -1.36   0.174   
>  -.4147514                                            
>                .075286
   techXknow |   .7549457   .2898237     2.60   0.009   
>   .1865548                                            
>               1.323336
    forXknow |   .9791053   .2785931     3.51   0.000   
>   .4327394                                            
>               1.525471
  eliteXknow |   .4679154    .288043     1.62   0.104   
>  -.0969832                                            
>               1.032814
   knowCovid |   .1417718   .2374852     0.60   0.551   
>  -.3239747                                            
>               .6075183
       _cons |    2.64676   .1022802    25.88   0.000   
>   2.446172                                            
>               2.847347
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (elite + eliteXknow)

 ( 1)  for - elite + forXknow - eliteXknow = 0

--------------------------------------------------------
> ----------------------
broadensoc~c |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .3505024   .1746544     2.01   0.045   
>   .0079771                                            
>               .6930277
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (tech + techXknow)

 ( 1)  - tech + for - techXknow + forXknow = 0

--------------------------------------------------------
> ----------------------
broadensoc~c |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .1265059   .1770064     0.71   0.475   
>  -.2206319                                            
>               .4736438
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXknow) - (tech + techXknow)

 ( 1)  - tech + elite - techXknow + eliteXknow = 0

--------------------------------------------------------
> ----------------------
broadensoc~c |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.2239965   .1873501    -1.20   0.232   
>  -.5914201                                            
>               .1434271
--------------------------------------------------------
> ----------------------

. eststo m2d: reg econsupport tech for elite techXjobeff
> ectCovid3 forXjobeffectCovid3 eliteXjobeffectCovid3 jo
> beffectCovid3, robust

Linear regression                               Number o
> f obs     =      1,995
                                                F(7, 198
> 7)        =       8.21
                                                Prob > F
>           =     0.0000
                                                R-square
> d         =     0.0287
                                                Root MSE
>           =     1.2363

--------------------------------------------------------
> ----------------------
             |               Robust
 econsupport |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |  -.0408779   .1141449    -0.36   0.720   
>  -.2647341                                            
>               .1829784
         for |  -.1082697   .1118358    -0.97   0.333   
>  -.3275973                                            
>                .111058
       elite |   -.145045   .1155573    -1.26   0.210   
>  -.3716711                                            
>               .0815811
techXjobef~3 |   .3952172   .2929991     1.35   0.178   
>  -.1794005                                            
>               .9698349
forXjobeff~3 |   .5859361    .295612     1.98   0.048   
>   .0061941                                            
>               1.165678
eliteXjobe~3 |    .241495   .3182532     0.76   0.448   
>  -.3826499                                            
>                 .86564
jobeffectC~3 |   .1981691   .2569821     0.77   0.441   
>  -.3058135                                            
>               .7021517
       _cons |   3.062284   .0938651    32.62   0.000   
>     2.8782                                            
>               3.246369
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (elite + eliteXjo
> beffectCovid3)

 ( 1)  for - elite + forXjobeffectCovid3 -
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
 econsupport |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .3812163   .1957742     1.95   0.052   
>  -.0027278                                            
>               .7651605
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (tech + techXjobe
> ffectCovid3)

 ( 1)  - tech + for - techXjobeffectCovid3 +
       forXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
 econsupport |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |    .123327   .1698166     0.73   0.468   
>  -.2097102                                            
>               .4563643
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXjobeffectCovid3) - (tech + techX
> jobeffectCovid3)

 ( 1)  - tech + elite - techXjobeffectCovid3 +
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
 econsupport |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.2578893   .1937648    -1.33   0.183   
>  -.6378929                                            
>               .1221143
--------------------------------------------------------
> ----------------------

. eststo m2e: reg healthsupport tech for elite techXjobe
> ffectCovid3 forXjobeffectCovid3 eliteXjobeffectCovid3 
> jobeffectCovid3, robust

Linear regression                               Number o
> f obs     =      1,995
                                                F(7, 198
> 7)        =       1.56
                                                Prob > F
>           =     0.1423
                                                R-square
> d         =     0.0056
                                                Root MSE
>           =      1.064

--------------------------------------------------------
> ----------------------
             |               Robust
healthsupp~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |   .1406493   .0942015     1.49   0.136   
>  -.0440949                                            
>               .3253935
         for |   -.053253    .094958    -0.56   0.575   
>  -.2394807                                            
>               .1329747
       elite |   .0318965   .0972942     0.33   0.743   
>  -.1589127                                            
>               .2227058
techXjobef~3 |  -.0529706   .2386201    -0.22   0.824   
>  -.5209424                                            
>               .4150013
forXjobeff~3 |    .224573   .2324292     0.97   0.334   
>  -.2312574                                            
>               .6804035
eliteXjobe~3 |  -.0713385   .2419785    -0.29   0.768   
>  -.5458968                                            
>               .4032197
jobeffectC~3 |  -.1325521   .1903238    -0.70   0.486   
>  -.5058074                                            
>               .2407031
       _cons |   3.835261   .0787824    48.68   0.000   
>   3.680756                                            
>               3.989766
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (elite + eliteXjo
> beffectCovid3)

 ( 1)  for - elite + forXjobeffectCovid3 -
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
healthsupp~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .2107621   .1666479     1.26   0.206   
>  -.1160608                                            
>                .537585
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (tech + techXjobe
> ffectCovid3)

 ( 1)  - tech + for - techXjobeffectCovid3 +
       forXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
healthsupp~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .0836413   .1668626     0.50   0.616   
>  -.2436026                                            
>               .4108853
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXjobeffectCovid3) - (tech + techX
> jobeffectCovid3)

 ( 1)  - tech + elite - techXjobeffectCovid3 +
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
healthsupp~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.1271207   .1749326    -0.73   0.468   
>  -.4701912                                            
>               .2159497
--------------------------------------------------------
> ----------------------

. eststo m2f: reg broadensocsec tech for elite techXjobe
> ffectCovid3 forXjobeffectCovid3 eliteXjobeffectCovid3 
> jobeffectCovid3, robust

Linear regression                               Number o
> f obs     =      1,992
                                                F(7, 198
> 4)        =      20.72
                                                Prob > F
>           =     0.0000
                                                R-square
> d         =     0.0624
                                                Root MSE
>           =     1.3185

--------------------------------------------------------
> ----------------------
             |               Robust
broadensoc~c |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |  -.1689846   .1195096    -1.41   0.158   
>  -.4033622                                            
>                .065393
         for |  -.2304481   .1173826    -1.96   0.050   
>  -.4606543                                            
>              -.0002419
       elite |  -.2135595   .1196948    -1.78   0.075   
>  -.4483002                                            
>               .0211811
techXjobef~3 |   .6053132   .2997079     2.02   0.044   
>   .0175378                                            
>               1.193088
forXjobeff~3 |   .8014246   .2953059     2.71   0.007   
>   .2222823                                            
>               1.380567
eliteXjobe~3 |   .7447406   .3062601     2.43   0.015   
>   .1441153                                            
>               1.345366
jobeffectC~3 |   .3323114   .2581734     1.29   0.198   
>  -.1740079                                            
>               .8386308
       _cons |     2.6016   .0986629    26.37   0.000   
>   2.408107                                            
>               2.795094
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (elite + eliteXjo
> beffectCovid3)

 ( 1)  for - elite + forXjobeffectCovid3 -
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
broadensoc~c |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .0397954   .1827501     0.22   0.828   
>  -.3186069                                            
>               .3981977
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (tech + techXjobe
> ffectCovid3)

 ( 1)  - tech + for - techXjobeffectCovid3 +
       forXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
broadensoc~c |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .1346479   .1774645     0.76   0.448   
>  -.2133885                                            
>               .4826843
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXjobeffectCovid3) - (tech + techX
> jobeffectCovid3)

 ( 1)  - tech + elite - techXjobeffectCovid3 +
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
broadensoc~c |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .0948525   .1882784     0.50   0.614   
>  -.2743917                                            
>               .4640966
--------------------------------------------------------
> ----------------------

. 
. esttab m2a m2b m2c m2d m2e m2f using "figures_tables/A
> ppendixtableG1.rtf", order(for elite tech knowCovid fo
> rXknow eliteXknow techXknow jobeffectCovid3 forXjobeff
> ectCovid3 eliteXjobeffectCovid3 techXjobeffectCovid3) 
> $esttabformat replace label onecell
(output written to figures_tables/AppendixtableG1.rtf)

. *Note: lincom estimates added manually to table*
. 
. 
. 
. log close
      name:  <unnamed>
       log:  C:\Users\kab235\Dropbox\CovidPopulism\Submi
> ssion PSRM\BaldwinMares_DataReplication\Covidrisk_appe
> ndix_replication.log
  log type:  text
 closed on:  20 Jul 2022, 12:54:41
--------------------------------------------------------
